@article {pmid37239238, year = {2023}, author = {Novičić, M and Savić, AM}, title = {Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training.}, journal = {Brain sciences}, volume = {13}, number = {5}, pages = {}, doi = {10.3390/brainsci13050766}, pmid = {37239238}, issn = {2076-3425}, abstract = {Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training of mental task execution via providing online feedback based on ERP measures. Our recent work introduced a novel electrotactile BCI for sensory training, based on somatosensory ERP; however, no previous studies have addressed specific somatosensory ERP morphological features as measures of sustained endogenous spatial tactile attention in the context of BCI control. Here we show the morphology of somatosensory ERP responses induced by a novel task introduced within our electrotactile BCI platform i.e., the sustained endogenous spatial electrotactile attention task. By applying pulsed electrical stimuli to the two proximal stimulation hotspots at the user's forearm, stimulating sequentially the mixed branches of radial and median nerves with equal probability of stimuli occurrence, we successfully recorded somatosensory ERPs for both stimulation locations, in the attended and unattended conditions. Waveforms of somatosensory ERP responses for both mixed nerve branches showed similar morphology in line with previous reports on somatosensory ERP components obtained by stimulation of exclusively sensory nerves. Moreover, we found statistically significant increases in ERP amplitude on several components, at both stimulation hotspots, while sustained endogenous spatial electrotactile attention task is performed. Our results revealed the existence of general ERP windows of interest and signal features that can be used to detect sustained endogenous tactile attention and classify between spatial attention locations in 11 healthy subjects. The current results show that features of N140, P3a and P3b somatosensory ERP components are the most prominent global markers of sustained spatial electrotactile attention, over all subjects, within our novel electrotactile BCI task/paradigm, and this work proposes the features of those components as markers of sustained endogenous spatial tactile attention in online BCI control. Immediate implications of this work are the possible improvement of online BCI control within our novel electrotactile BCI system, while these finding can be used for other tactile BCI applications in the diagnosis and treatment of neurological disorders by employing mixed nerve somatosensory ERPs and sustained endogenous electrotactile attention task as control paradigms.}, }
@article {pmid37239182, year = {2023}, author = {Zhu, S and Yang, J and Ding, P and Wang, F and Gong, A and Fu, Y}, title = {Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph.}, journal = {Brain sciences}, volume = {13}, number = {5}, pages = {}, doi = {10.3390/brainsci13050710}, pmid = {37239182}, issn = {2076-3425}, abstract = {The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli used in the SSVEP-BCI are plane stimulation targets (PSTs), with only a few studies using stereo stimulation targets (SSTs). To explore the parameter optimization of the SSVEP-BCI virtual SSTs, this paper presents a parameter knowledge graph. First, an online VR stereoscopic stimulation SSVEP-BCI system is built, and a parameter dictionary for VR stereoscopic stimulation parameters (shape, color, and frequency) is established. The online experimental results of 10 subjects under different parameter combinations were collected, and a knowledge graph was constructed to optimize the SST parameters. The best classification performances of the shape, color, and frequency parameters were sphere (91.85%), blue (94.26%), and 13Hz (95.93%). With various combinations of virtual reality stereo stimulation parameters, the performance of the SSVEP-BCI varies. Using the knowledge graph of the stimulus parameters can help intuitively and effectively select appropriate SST parameters. The knowledge graph of the stereo target stimulation parameters presented in this work is expected to offer a way to convert the application of the SSVEP-BCI and VR.}, }
@article {pmid37237679, year = {2023}, author = {Tan, X and Wang, D and Chen, J and Xu, M}, title = {Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {5}, pages = {}, doi = {10.3390/bioengineering10050609}, pmid = {37237679}, issn = {2306-5354}, abstract = {Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.}, }
@article {pmid37237678, year = {2023}, author = {Ali, MU and Kim, KS and Kallu, KD and Zafar, A and Lee, SW}, title = {OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {5}, pages = {}, doi = {10.3390/bioengineering10050608}, pmid = {37237678}, issn = {2306-5354}, abstract = {Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed an optimization-based feature selection algorithm to systematically investigate the complementary nature of multimodal fused features. After preprocessing the acquired data of both modalities (i.e., EEG and fNIRS), the temporal statistical features were computed separately with a 10 s interval for each modality. The computed features were fused to create a training vector. A wrapper-based binary enhanced whale optimization algorithm (E-WOA) was used to select the optimal/efficient fused feature subset using the support-vector-machine-based cost function. An online dataset of 29 healthy individuals was used to evaluate the performance of the proposed methodology. The findings suggest that the proposed approach enhances the classification performance by evaluating the degree of complementarity between characteristics and selecting the most efficient fused subset. The binary E-WOA feature selection approach showed a high classification rate (94.22 ± 5.39%). The classification performance exhibited a 3.85% increase compared with the conventional whale optimization algorithm. The proposed hybrid classification framework outperformed both the individual modalities and traditional feature selection classification (p < 0.01). These findings indicate the potential efficacy of the proposed framework for several neuroclinical applications.}, }
@article {pmid37237623, year = {2023}, author = {Perpetuini, D and Günal, M and Chiou, N and Koyejo, S and Mathewson, K and Low, KA and Fabiani, M and Gratton, G and Chiarelli, AM}, title = {Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {5}, pages = {}, doi = {10.3390/bioengineering10050553}, pmid = {37237623}, issn = {2306-5354}, abstract = {A brain-computer interface (BCI) allows users to control external devices through brain activity. Portable neuroimaging techniques, such as near-infrared (NIR) imaging, are suitable for this goal. NIR imaging has been used to measure rapid changes in brain optical properties associated with neuronal activation, namely fast optical signals (FOS) with good spatiotemporal resolution. However, FOS have a low signal-to-noise ratio, limiting their BCI application. Here FOS were acquired with a frequency-domain optical system from the visual cortex during visual stimulation consisting of a rotating checkerboard wedge, flickering at 5 Hz. We used measures of photon count (Direct Current, DC light intensity) and time of flight (phase) at two NIR wavelengths (690 nm and 830 nm) combined with a machine learning approach for fast estimation of visual-field quadrant stimulation. The input features of a cross-validated support vector machine classifier were computed as the average modulus of the wavelet coherence between each channel and the average response among all channels in 512 ms time windows. An above chance performance was obtained when differentiating visual stimulation quadrants (left vs. right or top vs. bottom) with the best classification accuracy of ~63% (information transfer rate of ~6 bits/min) when classifying the superior and inferior stimulation quadrants using DC at 830 nm. The method is the first attempt to provide generalizable retinotopy classification relying on FOS, paving the way for the use of FOS in real-time BCI.}, }
@article {pmid37237385, year = {2023}, author = {Yang, Z and Liu, F and Li, Z and Liu, N and Yao, X and Zhou, Y and Zhang, L and Jiang, P and Liu, H and Kong, L and Lang, C and Xu, X and Jia, J and Nakajima, T and Gu, W and Zheng, L and Zhang, Z}, title = {Histone lysine methyltransferase SMYD3 promotes oral squamous cell carcinoma tumorigenesis via H3K4me3-mediated HMGA2 transcription.}, journal = {Clinical epigenetics}, volume = {15}, number = {1}, pages = {92}, pmid = {37237385}, issn = {1868-7083}, abstract = {BACKGROUND: Epigenetic dysregulation is essential to the tumorigenesis of oral squamous cell carcinoma (OSCC). SET and MYND domain-containing protein 3 (SMYD3), a histone lysine methyltransferase, is implicated in gene transcription regulation and tumor development. However, the roles of SMYD3 in OSCC initiation are not fully understood. The present study investigated the biological functions and mechanisms involved in the SMYD3-mediated tumorigenesis of OSCC utilizing bioinformatic approaches and validation assays with the aim of informing the development of targeted therapies for OSCC.<br><br>RESULTS: 429 chromatin regulators were screened by a machine learning approach and aberrant expression of SMYD3 was found to be closely associated with OSCC formation and poor prognosis. Data profiling of single-cell and tissue demonstrated that upregulated SMYD3 significantly correlated with aggressive clinicopathological features of OSCC. Alterations in copy number and DNA methylation patterns may contribute to SMYD3 overexpression. Functional experimental results suggested that SMYD3 enhanced cancer cell stemness and proliferation in vitro and tumor growth in vivo. SMYD3 was observed to bind to the High Mobility Group AT-Hook 2 (HMGA2) promoter and elevated tri-methylation of histone H3 lysine 4 at the corresponding site was responsible for transactivating HMGA2. SMYD3 also was positively linked to HMGA2 expression in OSCC samples. Furthermore, treatment with the SMYD3 chemical inhibitor BCI-121 exerted anti-tumor effects.<br><br>CONCLUSIONS: Histone methyltransferase activity and transcription-potentiating function of SMYD3 were found to be essential for tumorigenesis and the SMYD3-HMGA2 is a potential therapeutic target in OSCC.}, }
@article {pmid37236786, year = {2023}, author = {Nadra, JG and Bengson, JJ and Morales, AB and Mangun, GR}, title = {Attention Without Constraint: Alpha Lateralization in Uncued Willed Attention.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0258-22.2023}, pmid = {37236786}, issn = {2373-2822}, abstract = {Studies of voluntary visual-spatial attention have used attention-directing cues, such as arrows, to induce or instruct observers to focus selective attention on relevant locations in visual space in order to detect or discriminate subsequent target stimuli. In everyday vision, however, voluntary attention is influenced by a host of factors, most of which are quite different from the laboratory paradigms that utilize attention-directing cues. These factors include priming, experience, reward, meaning, motivations, and high-level behavioral goals. Attention that is endogenously directed in the absence of external attention-directing cues has been referred to as self-initiated attention, or as in our prior work, as "willed attention" where volunteers decide where to attend in respond to a prompt to do so. Here, we used a novel paradigm that eliminated external influences (i.e., attention-directing cues and prompts) about where and/or when spatial attention should be directed. Using machine learning decoding methods, we showed that the well-known lateralization of EEG alpha power during spatial attention was also present during purely self-generated attention. By eliminating explicit cues or prompts that affect the allocation of voluntary attention, this work advances our understanding of the neural correlates of attentional control, and provides steps toward the development of EEG-based brain-computer interfaces that tap into human intentions.Significance StatementUnderstanding how behavioral goals influence how we allocate our voluntary attention is a central aim in cognitive neuroscience. A dominant paradigm for studying voluntary attention uses external cues (e.g., arrows) to focus spatial attention. However, real-world attention can be oriented by purely by self-initiated volitional processes, known as willed attention. We employed a novel paradigm that allowed participants the freedom to choose where and when to attend within an ongoing stimulus stream, eliminating potential external biases imposed by cues. We used support vector machine decoding off EEG alpha signals to investigate the temporal dynamics of willed attention shifts as volunteers made self-initiated shifts of spatial attention. Such an approach permits the investigation of the neural correlates of purely voluntary attention.}, }
@article {pmid37236176, year = {2023}, author = {Cui, Y and Xie, S and Xie, X and Zheng, D and Tang, H and Duan, K and Chen, X and Jiang, Y}, title = {LDER: A classification framework based on ERP enhancement in RSVP task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acd95d}, pmid = {37236176}, issn = {1741-2552}, abstract = {Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications. Approach: To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction (LDER) was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework. Main results: Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (SWFP, HDPCA, HDCA, and STHCP) in RSVP-BCI for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of AUC, balanced accuracy (BA), TPR, and FPR in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes. Significance: As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task. .}, }
@article {pmid37235473, year = {2023}, author = {Lycke, R and Kim, R and Zolotavin, P and Montes, J and Sun, Y and Koszeghy, A and Altun, E and Noble, B and Yin, R and He, F and Totah, N and Xie, C and Luan, L}, title = {Low-threshold, high-resolution, chronically stable intracortical microstimulation by ultraflexible electrodes.}, journal = {Cell reports}, volume = {42}, number = {6}, pages = {112554}, doi = {10.1016/j.celrep.2023.112554}, pmid = {37235473}, issn = {2211-1247}, abstract = {Intracortical microstimulation (ICMS) enables applications ranging from neuroprosthetics to causal circuit manipulations. However, the resolution, efficacy, and chronic stability of neuromodulation are often compromised by adverse tissue responses to the indwelling electrodes. Here we engineer ultraflexible stim-nanoelectronic threads (StimNETs) and demonstrate low activation threshold, high resolution, and chronically stable ICMS in awake, behaving mouse models. In vivo two-photon imaging reveals that StimNETs remain seamlessly integrated with the nervous tissue throughout chronic stimulation periods and elicit stable, focal neuronal activation at low currents of 2 μA. Importantly, StimNETs evoke longitudinally stable behavioral responses for over 8 months at a markedly low charge injection of 0.25 nC/phase. Quantified histological analyses show that chronic ICMS by StimNETs induces no neuronal degeneration or glial scarring. These results suggest that tissue-integrated electrodes provide a path for robust, long-lasting, spatially selective neuromodulation at low currents, which lessens risk of tissue damage or exacerbation of off-target side effects.}, }
@article {pmid37234419, year = {2023}, author = {Haggie, L and Schmid, L and Röhrle, O and Besier, T and McMorland, A and Saini, H}, title = {Linking cortex and contraction-Integrating models along the corticomuscular pathway.}, journal = {Frontiers in physiology}, volume = {14}, number = {}, pages = {1095260}, pmid = {37234419}, issn = {1664-042X}, abstract = {Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.}, }
@article {pmid37234411, year = {2023}, author = {Centeio, R and Cabrita, I and Schreiber, R and Kunzelmann, K}, title = {TMEM16A/F support exocytosis but do not inhibit Notch-mediated goblet cell metaplasia of BCi-NS1.1 human airway epithelium.}, journal = {Frontiers in physiology}, volume = {14}, number = {}, pages = {1157704}, pmid = {37234411}, issn = {1664-042X}, abstract = {Cl[-] channels such as the Ca[2+] activated Cl[-] channel TMEM16A and the Cl[-] permeable phospholipid scramblase TMEM16F may affect the intracellular Cl[-] concentration ([Cl[-]]i), which could act as an intracellular signal. Loss of airway expression of TMEM16A induced a massive expansion of the secretory cell population like goblet and club cells, causing differentiation into a secretory airway epithelium. Knockout of the Ca[2+]-activated Cl[-] channel TMEM16A or the phospholipid scramblase TMEM16F leads to mucus accumulation in intestinal goblet cells and airway secretory cells. We show that both TMEM16A and TMEM16F support exocytosis and release of exocytic vesicles, respectively. Lack of TMEM16A/F expression therefore causes inhibition of mucus secretion and leads to goblet cell metaplasia. The human basal epithelial cell line BCi-NS1.1 forms a highly differentiated mucociliated airway epithelium when grown in PneumaCult™ media under an air liquid interface. The present data suggest that mucociliary differentiation requires activation of Notch signaling, but not the function of TMEM16A. Taken together, TMEM16A/F are important for exocytosis, mucus secretion and formation of extracellular vesicles (exosomes or ectosomes) but the present data do no not support a functional role of TMEM16A/F in Notch-mediated differentiation of BCi-NS1.1 cells towards a secretory epithelium.}, }
@article {pmid37232857, year = {2023}, author = {Yang, G and Wang, Y and Xu, Z and Zhang, X and Ruan, W and Mo, F and Lu, B and Fan, P and Dai, Y and He, E and Song, Y and Wang, C and Liu, J and Cai, X}, title = {PtNPs/PEDOT:PSS-Modified Microelectrode Arrays for Detection of the Discharge of Head Direction Cells in the Retrosplenial Cortex of Rats under Dissociation between Visual and Vestibular Inputs.}, journal = {Biosensors}, volume = {13}, number = {5}, pages = {}, pmid = {37232857}, issn = {2079-6374}, abstract = {The electrophysiological activities of head direction (HD) cells under visual and vestibular input dissociation are important to understanding the formation of the sense of direction in animals. In this paper, we fabricated a PtNPs/PEDOT:PSS-modified MEA to detect changes in the discharge of HD cells under dissociated sensory conditions. The electrode shape was customized for the retrosplenial cortex (RSC) and was conducive to the sequential detection of neurons at different depths in vivo when combined with a microdriver. The recording sites of the electrode were modified with PtNPs/PEDOT:PSS to form a three-dimensional convex structure, leading to closer contact with neurons and improving the detection performance and signal-to-noise ratio of the MEA. We designed a rotating cylindrical arena to separate the visual and vestibular information of the rats and detected the changes in the directional tuning of the HD cells in the RSC. The results showed that after visual and vestibular sensory dissociation, HD cells used visual information to establish newly discharged directions which differed from the original direction. However, with the longer time required to process inconsistent sensory information, the function of the HD system gradually degraded. After recovery, the HD cells reverted to their newly established direction rather than the original direction. The research based on our MEAs revealed how HD cells process dissociated sensory information and contributes to the study of the spatial cognitive navigation mechanism.}, }
@article {pmid37230207, year = {2023}, author = {de Wauw, CV and Riecke, L and Goebel, R and Kaas, A and Sorger, B}, title = {Talking with hands and feet: Selective somatosensory attention and fMRI enable robust and convenient brain-based communication.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120172}, doi = {10.1016/j.neuroimage.2023.120172}, pmid = {37230207}, issn = {1095-9572}, abstract = {In brain-based communication, voluntarily modulated brain signals (instead of motor output) are utilized to interact with the outside world. The possibility to circumvent the motor system constitutes an important alternative option for severely paralyzed. Most communication brain-computer interface (BCI) paradigms require intact visual capabilities and impose a high cognitive load, but for some patients, these requirements are not given. In these situations, a better-suited, less cognitively demanding information-encoding approach may exploit auditorily-cued selective somatosensory attention to vibrotactile stimulation. Here, we propose, validate and optimize a novel communication-BCI paradigm using differential fMRI activation patterns evoked by selective somatosensory attention to tactile stimulation of the right hand or left foot. Using cytoarchitectonic probability maps and multi-voxel patterns analysis (MVPA), we show that the locus of selective somatosensory attention can be decoded from internally generated fMRI signal patterns from fMRI-signal patterns in (especially primary) somatosensory cortex with high accuracy and reliability, with the highest classification accuracy (85.93%) achieved when using Brodmann area 2 (SI-BA2) at a probability level of 0.2. Based on this outcome, we developed and validated a novel somatosensory attention-based yes/no communication procedure and demonstrated its high effectiveness even when using only a limited amount of (MVPA) training data. For the BCI user, the paradigm is straightforward, eye-independent, and requires only limited cognitive functioning. In addition, it is BCI-operator friendly given its objective and expertise-independent procedure. For these reasons, our novel communication paradigm has high potential for clinical applications.}, }
@article {pmid37228852, year = {2023}, author = {Deverett, B}, title = {Anesthesia for non-traditional consciousness.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1146242}, pmid = {37228852}, issn = {1662-5161}, }
@article {pmid37228786, year = {2023}, author = {Baker, JL and Toth, R and Deli, A and Zamora, M and Fleming, JE and Benjaber, M and Goerzen, D and Ryou, JW and Purpura, KP and Schiff, ND and Denison, T}, title = {Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.}, journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering}, volume = {2023}, number = {}, pages = {10123754}, pmid = {37228786}, issn = {1948-3546}, abstract = {Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.}, }
@article {pmid37228451, year = {2023}, author = {Dale, R and O'sullivan, TD and Howard, S and Orihuela-Espina, F and Dehghani, H}, title = {System Derived Spatial-Temporal CNN for High-Density fNIRS BCI.}, journal = {IEEE open journal of engineering in medicine and biology}, volume = {4}, number = {}, pages = {85-95}, pmid = {37228451}, issn = {2644-1276}, abstract = {An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.}, }
@article {pmid37227915, year = {2023}, author = {Jia, H and Feng, F and Caiafa, CF and Duan, F and Zhang, Y and Sun, Z and Sole-Casals, J}, title = {Multi-class Classification of Upper Limb Movements with Filter Bank Task-related Component Analysis.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3278747}, pmid = {37227915}, issn = {2168-2208}, abstract = {The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0.4193 ±0.0780 (7 classes) and 0.4032 ±0.0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0.3590 ±0.0645 and 0.3159 ±0.0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.}, }
@article {pmid37227910, year = {2023}, author = {Rathee, G and Kerrache, CA and Bilal, M}, title = {An Accurate and Inter-Operatable Fuzzy-based System using Genetic and Canonical Correlation Analysis Methods in Internet-of-Brain Things.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3275009}, pmid = {37227910}, issn = {1558-0210}, abstract = {The brain computer interface is defined as the way of acquiring the brain signals that analyse and translates them into commands that are relayed to intelligent devices for carrying out various actions. Through number of BCI mechanism and approaches have been proposed by various scientists to empower the individuals for directly controlling their objects via their thoughts. However, the actual implementation and realization of this method faces number of challenging with low accuracy and less interoperability. In addition, the pre-processing signals and feature extraction process is further time consuming and less accurate. In order to overcome the mentioned issues, this paper proposes an accurate and highly inter-operable system using genetic fuzzy system along. The predictive model and analysis can be further improved using canonical correlation analysis. The proposed framework is validated and demonstrated using brain typing system analysis. The results are computed against accuracy, latency and interoperability of the signals received from brain with less SNR along with traditional method. The proposed mechanism shows approximately 87% improvement as compare to existing approaches during the simulation over various performance metrics.}, }
@article {pmid37227015, year = {2023}, author = {Zhu, Z and Han, J and Zhu, H and Cai, C and Feng, C and Guo, X and Ying, Y and Jiang, H and Zheng, Z and Zhang, J and Zhu, J and Wu, H}, title = {Individualized targeting is warranted in subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression: A tractography analysis.}, journal = {Human brain mapping}, volume = {}, number = {}, pages = {}, doi = {10.1002/hbm.26339}, pmid = {37227015}, issn = {1097-0193}, abstract = {Subcallosal cingulate gyrus (SCG) is a target of deep brain stimulation (DBS) for treatment-resistant depression. However, previous randomized controlled trials report that approximately 42% of patients are responders to this therapy of last resort, and suboptimal targeting of SCG is a potential underlying factor to this unsatisfactory efficacy. Tractography has been proposed as a supplementary method to enhance targeting strategy. We performed a connectivity-based segmentation in the SCG region via probabilistic tractography in 100 healthy volunteers from the Human Connectome Project. The SCG voxels with maximum connectivity to brain regions implicated in depression, including Brodmann Area 10 (BA10), cingulate cortex, thalamus, and nucleus accumbens were identified, and the conjunctions were deemed as tractography-based targets. We then performed deterministic tractography using these targets in additional 100 volunteers to calculate streamline counts compassing to relevant brain regions and fibers. We also evaluated the intra- and inter-subject variance using test-retest dataset. Two tractography-based targets were identified. Tractography-based target-1 had the highest streamline counts to right BA10 and bilateral cingulate cortex, while tractography-based target-2 had the highest streamline counts to bilateral nucleus accumbens and uncinate fasciculus. The mean linear distance from individual tractography-based target to anatomy-based target was 3.2 ± 1.8 mm and 2.5 ± 1.4 mm in left and right hemispheres. The mean ± SD of targets between intra- and inter-subjects were 2.2 ± 1.2 and 2.9 ± 1.4 in left hemisphere, and 2.3 ± 1.4 and 3.1 ± 1.7 in right hemisphere, respectively. Individual heterogeneity as well as inherent variability from diffusion imaging should be taken into account during SCG-DBS target planning procedure.}, }
@article {pmid37223547, year = {2023}, author = {Li, M and Wei, R and Zhang, Z and Zhang, P and Xu, G and Liao, W}, title = {CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {4}, number = {}, pages = {0024}, pmid = {37223547}, issn = {2692-7632}, abstract = {Brain-computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.}, }
@article {pmid37221270, year = {2023}, author = {Xu, P and Huang, S and Krumm, BE and Zhuang, Y and Mao, C and Zhang, Y and Wang, Y and Huang, XP and Liu, YF and He, X and Li, H and Yin, W and Jiang, Y and Zhang, Y and Roth, BL and Xu, HE}, title = {Structural genomics of the human dopamine receptor system.}, journal = {Cell research}, volume = {}, number = {}, pages = {}, pmid = {37221270}, issn = {1748-7838}, abstract = {The dopaminergic system, including five dopamine receptors (D1R to D5R), plays essential roles in the central nervous system (CNS); and ligands that activate dopamine receptors have been used to treat many neuropsychiatric disorders, including Parkinson's Disease (PD) and schizophrenia. Here, we report cryo-EM structures of all five subtypes of human dopamine receptors in complex with G protein and bound to the pan-agonist, rotigotine, which is used to treat PD and restless legs syndrome. The structures reveal the basis of rotigotine recognition in different dopamine receptors. Structural analysis together with functional assays illuminate determinants of ligand polypharmacology and selectivity. The structures also uncover the mechanisms of dopamine receptor activation, unique structural features among the five receptor subtypes, and the basis of G protein coupling specificity. Our work provides a comprehensive set of structural templates for the rational design of specific ligands to treat CNS diseases targeting the dopaminergic system.}, }
@article {pmid37220058, year = {2023}, author = {Li, D and Wang, J and Xu, J and Fang, X and Ji, Y}, title = {Cross-Channel Specific-Mutual Feature Transfer Learning for Motor Imagery EEG Signals Decoding.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3269512}, pmid = {37220058}, issn = {2162-2388}, abstract = {In recent years, with the rapid development of deep learning, various deep learning frameworks have been widely used in brain-computer interface (BCI) research for decoding motor imagery (MI) electroencephalogram (EEG) signals to understand brain activity accurately. The electrodes, however, record the mixed activities of neurons. If different features are directly embedded in the same feature space, the specific and mutual features of different neuron regions are not considered, which will reduce the expression ability of the feature itself. We propose a cross-channel specific-mutual feature transfer learning (CCSM-FT) network model to solve this problem. The multibranch network extracts the specific and mutual features of brain's multiregion signals. Effective training tricks are used to maximize the distinction between the two kinds of features. Suitable training tricks can also improve the effectiveness of the algorithm compared with novel models. Finally, we transfer two kinds of features to explore the potential of mutual and specific features to enhance the expressive power of the feature and use the auxiliary set to improve identification performance. The experimental results show that the network has a better classification effect in the BCI Competition IV-2a and the HGD datasets.}, }
@article {pmid37216253, year = {2023}, author = {Zhang, Z and Feng, P and Oprea, A and Constandinou, TG}, title = {Calibration-Free and Hardware-Efficient Neural Spike Detection for Brain Machine Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2023.3278531}, pmid = {37216253}, issn = {1940-9990}, abstract = {Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints - the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this paper, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18MU m CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86MU W from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training.}, }
@article {pmid37214970, year = {2023}, author = {Rao, S and Huang, S and Liu, X and Lin, S and Glynn, C and Felix, K and Sahasrabudhe, A and Maley, C and Xu, J and Chen, W and Hong, E and Crosby, A and Wang, Q}, title = {Control of Polymers' Amorphous-crystalline Transition for Hydrogel Bioelectronics Miniaturization and Multifunctional Integration.}, journal = {Research square}, volume = {}, number = {}, pages = {}, doi = {10.21203/rs.3.rs-2864872/v1}, pmid = {37214970}, abstract = {Bioelectronic devices made of soft elastic materials exhibit motion-adaptive properties suitable for brain-machine interfaces and for investigating complex neural circuits. While two-dimensional microfabrication strategies enable miniaturizing devices to access delicate nerve structures, creating 3D architecture for expansive implementation requires more accessible and scalable manufacturing approaches. Here we present a fabrication strategy through the control of metamorphic polymers' amorphous-crystalline transition (COMPACT), for hydrogel bioelectronics with miniaturized fiber shape and multifunctional interrogation of neural circuits. By introducing multiple cross-linkers, acidification treatment, and oriented polymeric crystalline growth under deformation, we observed about an 80% diameter decrease in chemically cross-linked polyvinyl alcohol (PVA) hydrogel fibers, stably maintained in a fully hydrated state. We revealed that the addition of cross-linkers and acidification facilitated the oriented polymetric crystalline growth under mechanical stretching, which contributed to the desired hydrogel fiber diameter decrease. Our approach enabled the control of hydrogels' properties, including refractive index (RI 1.37-1.40 at 480 nm), light transmission (> 96%), stretchability (95% - 111%), and elastic modulus (10-63 MPa). To exploit these properties, we fabricated step-index hydrogel optical probes with contrasting RIs and applied them in optogenetics and photometric recordings in the mouse brain region of the ventral tegmental area (VTA) with concurrent social behavioral assessment. To extend COMPACT hydrogel multifunctional scaffolds to assimilate conductive nanomaterials and integrate multiple components of optical waveguide and electrodes, we developed carbon nanotubes (CNTs)-PVA hydrogel microelectrodes for hindlimb muscle electromyographic and brain electrophysiological recordings of light-triggered neural activities in transgenic mice expressing Channelrhodopsin-2 (ChR2).}, }
@article {pmid37213933, year = {2023}, author = {Syrov, N and Yakovlev, L and Miroshnikov, A and Kaplan, A}, title = {Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1180056}, pmid = {37213933}, issn = {1662-5161}, abstract = {Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain-computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation.}, }
@article {pmid37213929, year = {2023}, author = {Wang, X and Dai, X and Liu, Y and Chen, X and Hu, Q and Hu, R and Li, M}, title = {Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1175399}, pmid = {37213929}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor imagery electroencephalography (MI-EEG) has significant application value in the field of rehabilitation, and is a research hotspot in the brain-computer interface (BCI) field. Due to the small training sample size of MI-EEG of a single subject and the large individual differences among different subjects, existing classification models have low accuracy and poor generalization ability in MI classification tasks.<br><br>METHODS: To solve this problem, this paper proposes a electroencephalography (EEG) joint feature classification algorithm based on instance transfer and ensemble learning. Firstly, the source domain and target domain data are preprocessed, and then common space mode (CSP) and power spectral density (PSD) are used to extract spatial and frequency domain features respectively, which are combined into EEG joint features. Finally, an ensemble learning algorithm based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) is used to classify MI-EEG.<br><br>RESULTS: To validate the effectiveness of the algorithm, this paper compared and analyzed different algorithms on the BCI Competition IV Dataset 2a, and further verified the stability and effectiveness of the algorithm on the BCI Competition IV Dataset 2b. The experimental results show that the algorithm has an average accuracy of 91.5% and 83.7% on Dataset 2a and Dataset 2b, respectively, which is significantly better than other algorithms.<br><br>DISCUSSION: The statement explains that the algorithm fully exploits EEG signals and enriches EEG features, improves the recognition of the MI signals, and provides a new approach to solving the above problem.}, }
@article {pmid37213927, year = {2023}, author = {Feng, B and Yu, T and Wang, H and Liu, K and Wu, W and Long, W}, title = {Editorial: Machine learning and deep learning in biomedical signal analysis.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1183840}, doi = {10.3389/fnhum.2023.1183840}, pmid = {37213927}, issn = {1662-5161}, }
@article {pmid37211699, year = {2023}, author = {Ni, P and Zhou, C and Liang, S and Jiang, Y and Liu, D and Shao, Z and Noh, H and Zhao, L and Tian, Y and Zhang, C and Wei, J and Li, X and Yu, H and Ni, R and Yu, X and Qi, X and Zhang, Y and Ma, X and Deng, W and Guo, W and Wang, Q and Sham, PC and Chung, S and Li, T}, title = {YBX1-Mediated DNA Methylation-Dependent SHANK3 Expression in PBMCs and Developing Cortical Interneurons in Schizophrenia.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2300455}, doi = {10.1002/advs.202300455}, pmid = {37211699}, issn = {2198-3844}, support = {MH107884/MH/NIMH NIH HHS/United States ; NS121541/NS/NINDS NIH HHS/United States ; }, abstract = {Schizophrenia (SCZ) is a severe psychiatric and neurodevelopmental disorder. The pathological process of SCZ starts early during development, way before the first onset of psychotic symptoms. DNA methylation plays an important role in regulating gene expression and dysregulated DNA methylation is involved in the pathogenesis of various diseases. The methylated DNA immunoprecipitation-chip (MeDIP-chip) is performed to investigate genome-wide DNA methylation dysregulation in peripheral blood mononuclear cells (PBMCs) of patients with first-episode SCZ (FES). Results show that the SHANK3 promoter is hypermethylated, and this hypermethylation (HyperM) is negatively correlated with the cortical surface area in the left inferior temporal cortex and positively correlated with the negative symptom subscores in FES. The transcription factor YBX1 is further found to bind to the HyperM region of SHANK3 promoter in induced pluripotent stem cells (iPSCs)-derived cortical interneurons (cINs) but not glutamatergic neurons. Furthermore, a direct and positive regulatory effect of YBX1 on the expression of SHANK3 is confirmed in cINs using shRNAs. In summary, the dysregulated SHANK3 expression in cINs suggests the potential role of DNA methylation in the neuropathological mechanism underlying SCZ. The results also suggest that HyperM of SHANK3 in PBMCs can serve as a potential peripheral biomarker of SCZ.}, }
@article {pmid37211686, year = {2023}, author = {Wang, X and Zhang, A and Yu, Q and Wang, Z and Wang, J and Xu, P and Liu, Y and Lu, J and Zheng, J and Li, H and Qi, Y and Zhang, J and Fang, Y and Xu, S and Zhou, J and Wang, K and Chen, S and Zhang, J}, title = {Single-Cell RNA Sequencing and Spatial Transcriptomics Reveal Pathogenesis of Meningeal Lymphatic Dysfunction after Experimental Subarachnoid Hemorrhage.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2301428}, doi = {10.1002/advs.202301428}, pmid = {37211686}, issn = {2198-3844}, abstract = {Subarachnoid hemorrhage (SAH) is a devastating subtype of stroke with high mortality and disability rate. Meningeal lymphatic vessels (mLVs) are a newly discovered intracranial fluid transport system and are proven to drain extravasated erythrocytes from cerebrospinal fluid into deep cervical lymph nodes after SAH. However, many studies have reported that the structure and function of mLVs are injured in several central nervous system diseases. Whether SAH can cause mLVs injury and the underlying mechanism remain unclear. Herein, single-cell RNA sequencing and spatial transcriptomics are applied, along with in vivo/vitro experiments, to investigate the alteration of the cellular, molecular, and spatial pattern of mLVs after SAH. First, it is demonstrated that SAH induces mLVs impairment. Then, through bioinformatic analysis of sequencing data, it is discovered that thrombospondin 1 (THBS1) and S100A6 are strongly associated with SAH outcome. Furthermore, the THBS1-CD47 ligand-receptor pair is found to function as a key role in meningeal lymphatic endothelial cell apoptosis via regulating STAT3/Bcl-2 signaling. The results illustrate a landscape of injured mLVs after SAH for the first time and provide a potential therapeutic strategy for SAH based on mLVs protection by disrupting THBS1 and CD47 interaction.}, }
@article {pmid37209578, year = {2023}, author = {Zhao, Y and Zeng, H and Zheng, H and Wu, J and Kong, W and Dai, G}, title = {A bidirectional interaction-based hybrid network architecture for EEG cognitive recognition.}, journal = {Computer methods and programs in biomedicine}, volume = {238}, number = {}, pages = {107593}, doi = {10.1016/j.cmpb.2023.107593}, pmid = {37209578}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Extracting cognitive representation and computational representation information simultaneously from electroencephalography (EEG) data and constructing corresponding information interaction models can effectively improve the recognition capability of brain cognitive status. However, due to the huge gap in the interaction between the two types of information, existing studies have yet to consider the advantages of the interaction of both.<br><br>METHODS: This paper introduces a novel architecture named the bidirectional interaction-based hybrid network (BIHN) for EEG cognitive recognition. BIHN consists of two networks: a cognitive-based network named CogN (e.g., graph convolution network, GCN; capsule network, CapsNet) and a computing-based network named ComN (e.g., EEGNet). CogN is responsible for extracting cognitive representation features from EEG data, while ComN is responsible for extracting computational representation features. Additionally, a bidirectional distillation-based coadaptation (BDC) algorithm is proposed to facilitate information interaction between CogN and ComN to realize the coadaptation of the two networks through bidirectional closed-loop feedback.<br><br>RESULTS: Cross-subject cognitive recognition experiments were performed on the Fatigue-Awake EEG dataset (FAAD, 2-class classification) and SEED dataset (3-class classification), and hybrid network pairs of GCN&#xa0;+&#xa0;EEGNet and CapsNet&#xa0;+&#xa0;EEGNet were verified. The proposed method achieved average accuracies of 78.76% (GCN&#xa0;+&#xa0;EEGNet) and 77.58% (CapsNet&#xa0;+&#xa0;EEGNet) on FAAD and 55.38% (GCN&#xa0;+&#xa0;EEGNet) and 55.10% (CapsNet&#xa0;+&#xa0;EEGNet) on SEED, outperforming the hybrid networks without the bidirectional interaction strategy.<br><br>CONCLUSIONS: Experimental results show that BIHN can achieve superior performance on two EEG datasets and enhance the ability of both CogN and ComN in EEG processing as well as cognitive recognition. We also validated its effectiveness with different hybrid network pairs. The proposed method could greatly promote the development of brain-computer collaborative intelligence.}, }
@article {pmid37209444, year = {2023}, author = {Chen, J and Zhang, Y and Pan, Y and Xu, P and Guan, C}, title = {A transformer-based deep neural network model for SSVEP classification.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {164}, number = {}, pages = {521-534}, doi = {10.1016/j.neunet.2023.04.045}, pmid = {37209444}, issn = {1879-2782}, abstract = {Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data becomes urgent. In recent years, developing the methods that can work in inter-subject scenario has become a promising new direction. As a popular deep learning model nowadays, Transformer has been used in EEG signal classification tasks owing to its excellent performance. Therefore, in this study, we proposed a deep learning model for SSVEP classification based on Transformer architecture in inter-subject scenario, termed as SSVEPformer, which was the first application of Transformer on the SSVEP classification. Inspired by previous studies, we adopted the complex spectrum features of SSVEP data as the model input, which could enable the model to simultaneously explore the spectral and spatial information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) was proposed to improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12 targets; Dataset 2: 35 subjects, 40 targets). The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate than other baseline methods. The proposed models validate the feasibility of deep learning models based on Transformer architecture for SSVEP data classification, and could serve as potential models to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.}, }
@article {pmid37209127, year = {2023}, author = {Hong, W and Liang, P and Pan, Y and Jin, J and Luo, L and Li, Y and Jin, C and Lü, W and Wang, M and Liu, Y and Chen, H and Gou, H and Wei, W and Ma, Z and Tao, R and Zha, R and Zhang, X}, title = {Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder.}, journal = {Journal of behavioral addictions}, volume = {}, number = {}, pages = {}, doi = {10.1556/2006.2023.00014}, pmid = {37209127}, issn = {2063-5303}, abstract = {BACKGROUND AND AIMS: Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion is a core of value-based decision-making and its alteration plays an important role in addiction. However, few studies explored it in internet gaming disorder patients (IGD).<br><br>METHODS: In this study, IGD patients (PIGD) and healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences in loss aversion, brain functional networks of node-centric functional connectivity (nFC) and the overlapping community features of edge-centric functional connectivity (eFC) in IGT.<br><br>RESULTS: PIGD performed worse with lower average net score in IGT. The computational model results showed that PIGD significantly reduced loss aversion. There was no group difference in nFC. However, there were significant group differences in the overlapping community features of eFC1. Furthermore, in Con-PIGD, loss aversion was positively correlated with the edge community pro&#xfb01;le similarity of the edge2 between left IFG and right hippocampus at right caudate. This relationship was suppressed by response consistency3 in PIGD. In addition, reduced loss aversion was negatively correlated with the promoted bottom-to-up neuromodulation from the right hippocampus to the left IFG in PIGD.<br><br>DISCUSSION AND CONCLUSIONS: The reduced loss aversion in value-based decision making and their related edge-centric functional connectivity support that the IGD showed the same value-based decision-making deficit as the substance use and other behavioral addictive disorders. These findings may have important significance for understanding the definition and mechanism of IGD in the future.}, }
@article {pmid37205052, year = {2023}, author = {Li, J and Qi, Y and Pan, G}, title = {Phase-amplitude coupling-based adaptive filters for neural signal decoding.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1153568}, pmid = {37205052}, issn = {1662-4548}, abstract = {Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30-200 Hz) which can be too coarse to capture features that appear in narrow bands. An ideal option is to find the optimal frequency bands for specific tasks in real-time and dynamically. To tackle this problem, we propose an adaptive band filter that selects the useful frequency band in a data-driven way. Specifically, we leverage the phase-amplitude coupling (PAC) of the coupled working mechanism of synchronizing neuron and pyramidal neurons in neuronal oscillations, in which the phase of slower oscillations modulates the amplitude of faster ones, to help locate the fine frequency bands from the gamma range, in a task-specific and individual-specific way. Thus, the information can be more precisely extracted from ECoG signals to improve neural decoding performance. Based on this, an end-to-end decoder (PACNet) is proposed to construct a neural decoding application with adaptive filter banks in a uniform framework. Experiments show that PACNet can improve neural decoding performance universally with different tasks.}, }
@article {pmid37201274, year = {2023}, author = {Marcos-Martínez, D and Santamaría-Vázquez, E and Martínez-Cagigal, V and Pérez-Velasco, S and Rodríguez-González, V and Martín-Fernández, A and Moreno-Calderón, S and Hornero, R}, title = {ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces.}, journal = {Computers in biology and medicine}, volume = {160}, number = {}, pages = {107011}, doi = {10.1016/j.compbiomed.2023.107011}, pmid = {37201274}, issn = {1879-0534}, abstract = {BACKGROUND AND OBJECTIVE: Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain-computer interface (BCI) system that analyzes the user's brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms.<br><br>METHODS: ITACA is designed to be easy-to-use, flexible and attractive. Specifically, ITACA includes three different gamified training scenarios with a choice of five brain activity metrics as real-time feedback. Among them, novel metrics based on functional connectivity and network theory stand out. It is complemented with five different computerized versions of widespread cognitive assessment tests. To validate the proposed framework, a computational efficiency analysis and an NF training protocol focused on frontal-medial theta modulation were conducted.<br><br>RESULTS: Efficiency analysis proved that all implemented metrics allow an optimal feedback update rate for conducting NF sessions. Furthermore, conducted NF protocol yielded results that support the use of ITACA in NF research studies.<br><br>CONCLUSIONS: ITACA implements a wide variety of features for designing, conducting and evaluating NF studies with the goal of helping researchers expand the current state-of-the-art in NF training.}, }
@article {pmid37200132, year = {2023}, author = {Ai, J and Meng, J and Mai, X and Zhu, X}, title = {BCI Control of a Robotic arm based on SSVEP with Moving Stimuli for Reach and grasp Tasks.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3277612}, pmid = {37200132}, issn = {2168-2208}, abstract = {Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.}, }
@article {pmid37196071, year = {2023}, author = {Murphy, RR}, title = {Sci-fi imagines how good brain-machine interfaces will amplify bad choices.}, journal = {Science robotics}, volume = {8}, number = {78}, pages = {eadi2192}, doi = {10.1126/scirobotics.adi2192}, pmid = {37196071}, issn = {2470-9476}, mesh = {Male ; Humans ; *Brain-Computer Interfaces ; *Robotics ; }, abstract = {Machine Man and The Andromeda Evolution explore personal and societal ramifications of brain-machine interfaces.}, }
@article {pmid37195562, year = {2023}, author = {Phillips, MM and Pavlyk, I and Allen, M and Ghazaly, E and Cutts, R and Carpentier, J and Berry, JS and Nattress, C and Feng, S and Hallden, G and Chelala, C and Bomalaski, J and Steele, J and Sheaff, M and Balkwill, F and Szlosarek, PW}, title = {Correction: A role for macrophages under cytokine control in mediating resistance to ADI-PEG20 (pegargiminase) in ASS1-deficient mesothelioma.}, journal = {Pharmacological reports : PR}, volume = {}, number = {}, pages = {}, doi = {10.1007/s43440-023-00487-z}, pmid = {37195562}, issn = {2299-5684}, }
@article {pmid37193899, year = {2023}, author = {O'Leary, K}, title = {MRI decoders translate thoughts into words.}, journal = {Nature medicine}, volume = {}, number = {}, pages = {}, doi = {10.1038/d41591-023-00044-4}, pmid = {37193899}, issn = {1546-170X}, }
@article {pmid37191865, year = {2023}, author = {Niu, L and Bin, J and Wang, JKS and Zhan, G and Jia, J and Zhang, L and Gan, Z and Kang, X}, title = {Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {37191865}, issn = {1741-0444}, abstract = {A brain-computer interface (BCI) system and virtual reality (VR) are integrated as a more interactive hybrid system (BCI-VR) that allows the user to manipulate the car. A virtual scene in the VR system that is the same as the physical environment is built, and the object's movement can be observed in the VR scene. The four-class three-dimensional (3D) paradigm is designed and moves synchronously in virtual reality. The dynamic paradigm may affect their attention according to the experimenters' feedback. Fifteen subjects in our experiment steered the car according to a specified motion trajectory. According to our online experimental result, different motion trajectories of the paradigm have various effects on the system's performance, and training can mitigate this adverse effect. Moreover, the hybrid system using frequencies between 5 and 10 Hz indicates better performance than those using lower or higher stimulation frequencies. The experiment results show a maximum average accuracy of 0.956 and a maximum information transfer rate (ITR) of 41.033 bits/min. It suggests that a hybrid system provides a high-performance way of brain-computer interaction. This research could encourage more interesting applications involving BCI and VR technologies.}, }
@article {pmid37190647, year = {2023}, author = {Al-Nafjan, A and Aldayel, M and Kharrat, A}, title = {Systematic Review and Future Direction of Neuro-Tourism Research.}, journal = {Brain sciences}, volume = {13}, number = {4}, pages = {}, pmid = {37190647}, issn = {2076-3425}, abstract = {Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists. Neuro-tourism provides accurate real-time data on tourists' conscious and unconscious emotions. Neuro-tourism uses the methods of neuromarketing such as brain-computer interface (BCI), eye-tracking, galvanic skin response, etc., to create tourism goods and services to improve tourist experience and satisfaction. Due to the novelty of neuro-tourism and the dearth of studies on this subject, this study offered a comprehensive analysis of the peer-reviewed journal publications in neuro-tourism research for the previous 12 years to detect trends in this field and provide insights for academics. We reviewed 52 articles indexed in the Web of Science (WoS) core collection database and examined them using our suggested classification schema. The results reveal a large growth in the number of published articles on neuro-tourism, demonstrating a rise in the relevance of this field. Additionally, the findings indicated a lack of integrating artificial intelligence techniques in neuro-tourism studies. We believe that the advancements in technology and research collaboration will facilitate exponential growth in this field.}, }
@article {pmid37190630, year = {2023}, author = {Vanutelli, ME and Salvadore, M and Lucchiari, C}, title = {BCI Applications to Creativity: Review and Future Directions, from little-c to C[2].}, journal = {Brain sciences}, volume = {13}, number = {4}, pages = {}, pmid = {37190630}, issn = {2076-3425}, abstract = {BCI devices are increasingly being used to create interactive interfaces between users and their own psychophysiological signals. Over the years, these systems have seen strong development as they can enable people with limited mobility to make certain decisions to alter their environment. Additionally, their portability and ease of use have allowed a field of research to flourish for the study of cognitive and emotional processes in natural settings. The study of creativity, especially little creativity (little-c), is one example, although the results of this cutting-edge research are often poorly systematized. The purpose of the present paper, therefore, was to conduct a scoping review to describe and systematize the various studies that have been conducted on the application potential of BCI to the field of creativity. Twenty-two papers were selected that collect information on different aspects of creativity, including clinical applications; art experience in settings with high ecological validity; BCI for creative content creation, and participants' engagement. Critical issues and potentialities of this promising area of study are also presented. Implications for future developments towards multi-brain creativity settings and C[2] are discussed.}, }
@article {pmid37190621, year = {2023}, author = {Lakshminarayanan, K and Shah, R and Daulat, SR and Moodley, V and Yao, Y and Sengupta, P and Ramu, V and Madathil, D}, title = {Evaluation of EEG Oscillatory Patterns and Classification of Compound Limb Tactile Imagery.}, journal = {Brain sciences}, volume = {13}, number = {4}, pages = {}, pmid = {37190621}, issn = {2076-3425}, abstract = {Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities and classification performance of the compound TI were compared with similar compound motor imagery (MI) with the same digits as TI in the same subjects. Methods: Twelve healthy right-handed adults with no history of upper limb injury, musculoskeletal condition, or neurological disorder participated in the study. The study evaluated the event-related desynchronization (ERD) response and brain-computer interface (BCI) classification performance on discriminating between the digits in the left-hand during the imagery of vibrotactile stimuli to either the index, middle, or thumb finger pads for TI and while performing a motor activity with the same digits for MI. A supervised machine learning technique was applied to discriminate between the digits within the same given limb for both imagery conditions. Results: Both TI and MI exhibited similar patterns of ERD in the alpha and beta bands at the index, middle, and thumb digits within the left hand. While TI had significantly lower ERD for all three digits in both bands, the classification performance of TI-based BCI (77.74 ± 6.98%) was found to be similar to the MI-based BCI (78.36 ± 5.38%). Conclusions: The results of this study suggest that compound tactile imagery can be a viable alternative to MI for BCI classification. The study contributes to the growing body of evidence supporting the use of TI in BCI applications, and future research can build on this work to explore the potential of TI-based BCI for motor rehabilitation and the control of external devices.}, }
@article {pmid37190591, year = {2023}, author = {Yang, J and Wang, M and Lv, Y and Chen, J}, title = {Cortical Layer Markers Expression and Increased Synaptic Density in Interstitial Neurons of the White Matter from Drug-Resistant Epilepsy Patients.}, journal = {Brain sciences}, volume = {13}, number = {4}, pages = {}, pmid = {37190591}, issn = {2076-3425}, abstract = {The interstitial neurons in the white matter of the human and non-human primate cortex share a similar developmental origin with subplate neurons and deep-layer cortical neurons. A subset of interstitial neurons expresses the molecular markers of subplate neurons, but whether interstitial neurons express cortical layer markers in the adult human brain remains unexplored. Here we report the expression of cortical layer markers in interstitial neurons in the white matter of the adult human brain, supporting the hypothesis that interstitial neurons could be derived from cortical progenitor cells. Furthermore, we found increased non-phosphorylated neurofilament protein (NPNFP) expression in interstitial neurons in the white matter of drug-resistant epilepsy patients. We also identified the expression of glutamatergic and g-aminobutyric acid (GABAergic) synaptic puncta that were distributed in the perikarya and dendrites of interstitial neurons. The density of glutamatergic and GABAergic synaptic puncta was increased in interstitial neurons in the white matter of drug-resistant epilepsy patients compared with control brain tissues with no history of epilepsy. Together, our results provide important insights of the molecular identity of interstitial neurons in the adult human white matter. Increased synaptic density of interstitial neurons could result in an imbalanced synaptic network in the white matter and participate as part of the epileptic network in drug-resistant epilepsy.}, }
@article {pmid37190517, year = {2023}, author = {Nie, A and Wu, Y}, title = {Differentiation of the Contribution of Familiarity and Recollection to the Old/New Effects in Associative Recognition: Insight from Semantic Relation.}, journal = {Brain sciences}, volume = {13}, number = {4}, pages = {}, pmid = {37190517}, issn = {2076-3425}, abstract = {Previous research has revealed two different old/new effects, the early mid-frontal old/new effect (a.k.a., FN400) and the late parietal old/new effect (a.k.a., LPC), which relate to familiarity and recollection processes, respectively. Although associative recognition is thought to be more based on recollection, recent studies have confirmed that familiarity can make a great contribution when the items of a pair are unitized. However, it remains unclear whether the old/new effects are sensitive to the nature of different semantic relations. The current ERP (event-related potentials) study aimed to address this, where picture pairs of thematic, taxonomic, and unrelated relations served as stimuli and participants were required to discriminate the pair type: intact, rearranged, "old + new", or new. We confirmed both FN400 and LPC. Our findings, by comparing the occurrence and the amplitudes of these two components, implicate that the neural activity of associative recognition is sensitive to the semantic relation of stimuli and depends more on stimulus properties, that the familiarity of a single item can impact the neural activities in discriminating associative pairs, and that the interval length between encoding and test modulates the familiarity of unrelated pairs. In addition, the dissociation between FN400 and LPC reinforces the dual-process models.}, }
@article {pmid37067974, year = {2023}, author = {Yan, W and He, B and Zhao, J and Wu, Y and Du, C and Xu, G}, title = {Frequency Domain Filtering Method for SSVEP-EEG Preprocessing.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {2079-2089}, doi = {10.1109/TNSRE.2023.3266488}, pmid = {37067974}, issn = {1558-0210}, mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Recognition, Psychology ; Photic Stimulation/methods ; }, abstract = {Steady-state visual evoked potential (SSVEP) signal collected from the scalp typically contains other types of electric signals, and it is important to remove these noise components from the actual signal by application of a pre-processing step for accurate analysis. High-pass or bandpass filtering of the SSVEP signal in the time domain is the most common pre-processing method. Because frequency is the most important feature information contained in the SSVEP signal, a technique for frequency-domain filtering of SSVEP was proposed here. In this method, the time-domain signal is extended to multi-dimensional signal by empirical mode decomposition (EMD), where each dimension represents a intrinsic mode function (IMF). The multi-dimensional signal is transformed to a frequency-domain signal by 2-D Fourier transform, the Gaussian high-pass filter function is constructed to perform high-pass filtering, and then the filtered signal is transformed to time domain by 2-D inverse Fourier transform. Finally, the filtered multi-dimensional intrinsic mode function is superimposed and averaged as the frequency-domain filtered signal. Compared with the time-domain filtering method, the experimental results revealed that frequency-domain filtering method effectively removed the baseline drift in signal and effectively suppressed the low-frequency interference component. This method was tested using data from public datasets and the results show that the proposed frequency-domain filtering method can significantly improve the feature recognition performance of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA) methods. Thus, the results suggest that the application of frequency-domain filtering in the pre-processing stage allows improved noise removal. The proposed method extends SSVEP signal filtering from time-domain to frequency-domain, and the results suggest that this analysis tool significantly promotes the practical application of SSVEP systems.}, }
@article {pmid37189929, year = {2023}, author = {Sahli, F and Sahli, H and Trabelsi, O and Jebabli, N and Zghibi, M and Haddad, M}, title = {Peer Verbal Encouragement Enhances Offensive Performance Indicators in Handball Small-Sided Games.}, journal = {Children (Basel, Switzerland)}, volume = {10}, number = {4}, pages = {}, doi = {10.3390/children10040680}, pmid = {37189929}, issn = {2227-9067}, abstract = {OBJECTIVE: This study aimed at assessing the effects of two verbal encouragement modalities on the different offensive and defensive performance indicators in handball small-sided games practiced in physical education settings.<br><br>METHODS: A total of 14 untrained secondary school male students, aged 17 to 18, took part in a three-session practical intervention. Students were divided into two teams of seven players (four field players, a goalkeeper, and two substitutes). During each experimental session, each team played one 8 min period under teacher verbal encouragement (TeacherEN) and another under peer verbal encouragement (PeerEN). All sessions were videotaped for later analysis using a specific grid focusing on the balls played, balls won, balls lost, shots on goal, goals scored, as well as the ball conservation index (BCI), and the defensive efficiency index (DEI).<br><br>RESULTS: The findings showed no significant differences in favor of TeacherEN in all the performance indicators that were measured, whereas significant differences in favor of PeerEN were observed in balls played and shots on goal.<br><br>CONCLUSIONS: When implemented in handball small-sided games, peer verbal encouragement can produce greater positive effects than teacher verbal encouragement in terms of offensive performance.}, }
@article {pmid37188006, year = {2023}, author = {Liang, F and Yu, S and Pang, S and Wang, X and Jie, J and Gao, F and Song, Z and Li, B and Liao, WH and Yin, M}, title = {Non-human primate models and systems for gait and neurophysiological analysis.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1141567}, pmid = {37188006}, issn = {1662-4548}, abstract = {Brain-computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&amp;V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&amp;V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.}, }
@article {pmid37184907, year = {2023}, author = {Xu, F and Wang, C and Yu, X and Zhao, J and Liu, M and Zhao, J and Gao, L and Jiang, X and Zhu, Z and Wu, Y and Wang, D and Feng, S and Yin, S and Zhang, Y and Leng, J}, title = {One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2350030}, doi = {10.1142/S0129065723500302}, pmid = {37184907}, issn = {1793-6462}, abstract = {Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.}, }
@article {pmid37183188, year = {2023}, author = {Bu, Y and Harrington, DL and Lee, RR and Shen, Q and Angeles-Quinto, A and Ji, Z and Hansen, H and Hernandez-Lucas, J and Baumgartner, J and Song, T and Nichols, S and Baker, D and Rao, R and Lerman, I and Lin, T and Tu, XM and Huang, M}, title = {Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad173}, pmid = {37183188}, issn = {1460-2199}, abstract = {Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging technique. The present study developed a MEG sensor-based BCI neural network to decode Rock-Paper-scissors gestures (MEG-RPSnet). Unique preprocessing pipelines in tandem with convolutional neural network deep-learning models accurately classified gestures. On a single-trial basis, we found an average of 85.56% classification accuracy in 12 subjects. Our MEG-RPSnet model outperformed two state-of-the-art neural network architectures for electroencephalogram-based BCI as well as a traditional machine learning method, and demonstrated equivalent and/or better performance than machine learning methods that have employed invasive, electrocorticography-based BCI using the same task. In addition, MEG-RPSnet classification performance using an intra-subject approach outperformed a model that used a cross-subject approach. Remarkably, we also found that when using only central-parietal-occipital regional sensors or occipitotemporal regional sensors, the deep learning model achieved classification performances that were similar to the whole-brain sensor model. The MEG-RSPnet model also distinguished neuronal features of individual hand gestures with very good accuracy. Altogether, these results show that noninvasive MEG-based BCI applications hold promise for future BCI developments in hand-gesture decoding.}, }
@article {pmid37180552, year = {2023}, author = {Barradas-Chacón, LA and Brunner, C and Wriessnegger, SC}, title = {Stylized faces enhance ERP features used for the detection of emotional responses.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1160800}, pmid = {37180552}, issn = {1662-5161}, abstract = {For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. The RSVP protocol is used in this experiment to present human faces of emotion to 28 participants while EEG was measured. We found that artificially enhanced human faces with exaggerated, cartoonish visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known to relate to the facial visual encoding process. Our findings suggest that the study of emotion elicitation could exploit consistent, high detail, AI generated stimuli transformations to study the characteristics of electrical brain activity related to visual affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect decoding from EEG can improve the experience of a user.}, }
@article {pmid37179906, year = {2023}, author = {Jia, X and Wang, W and Liang, J and Ma, X and Chen, W and Wu, D and Zhang, H and Ni, S and Wu, J and Lai, C and Zhang, Y}, title = {Application of amide proton transfer imaging to pretreatment risk stratification of childhood neuroblastoma: comparison with neuron-specific enolase.}, journal = {Quantitative imaging in medicine and surgery}, volume = {13}, number = {5}, pages = {3001-3012}, pmid = {37179906}, issn = {2223-4292}, abstract = {BACKGROUND: The diagnosis and treatment of childhood neuroblastoma (NB) varies with different risk groups, thus requiring accurate preoperative risk assessment. This study aimed to verify the feasibility of amide proton transfer (APT) imaging in risk stratification of abdominal NB in children, and compare it with the serum neuron-specific enolase (NSE).<br><br>METHODS: This prospective study enrolled 86 consecutive pediatric volunteers with suspected NB, and all subjects underwent abdominal APT imaging on a 3T magnetic resonance imaging scanner. A 4-pool Lorentzian fitting model was used to mitigate motion artifacts and separate the APT signal from the contaminating ones. The APT values were measured from tumor regions delineated by two experienced radiologists. The one-way analysis of variance, independent-sample t-test, Mann-Whitney U-test, and receiver operating characteristic analysis were performed to evaluate and compare the risk stratification performance of the APT value and serum NSE index-a routine biomarker of NB in clinics.<br><br>RESULTS: Thirty-four cases (mean age, 38.6&#xb1;32.4 months; 5 very-low-risk, 5 low-risk, 8 intermediate-risk and 16 high-risk ones) were included in the final analysis. The APT values were significantly higher in high-risk NB (5.80%&#xb1;1.27%) than in the non-high-risk group (3.88%&#xb1;1.01%) composed of the other three risk groups (P<0.001). However, there was no significant difference (P=0.18) in NSE levels between the high-risk (93.05&#xb1;97.14 ng/mL) and non-high-risk groups (41.45&#xb1;30.99 ng/mL). The associated area under the curve (AUC) of the APT parameter (AUC =0.89) in differentiating high-risk NB from non-high-risk NB was significantly higher (P=0.03) than that of NSE (AUC =0.64).<br><br>CONCLUSIONS: As an emerging non-invasive magnetic resonance imaging technique, APT imaging has a promising prospect for distinguishing high-risk NB from non-high-risk NB in routine clinical applications.}, }
@article {pmid37177761, year = {2023}, author = {Knierim, MT and Bleichner, MG and Reali, P}, title = {A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {9}, pages = {}, pmid = {37177761}, issn = {1424-8220}, mesh = {Electroencephalography/methods ; *Hearing Aids ; Electrodes ; *Brain-Computer Interfaces ; Noise ; }, abstract = {Wearable electroencephalography (EEG) has the potential to improve everyday life through brain-computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations. However, the use of such concealed EEG approaches is limited by measurement challenges such as reduced signal amplitudes and high recording system costs. In this article, we compare the performance of a lower-cost open-source amplification system, the OpenBCI Cyton+Daisy boards, with a benchmark amplifier, the MBrainTrain Smarting Mobi. Our results show that the OpenBCI system is a viable alternative for concealed EEG research, with highly similar noise performance, but slightly lower timing precision. This system can be a great option for researchers with a smaller budget and can, therefore, contribute significantly to advancing concealed EEG research.}, }
@article {pmid37177482, year = {2023}, author = {de Brito Guerra, TC and Nóbrega, T and Morya, E and de M Martins, A and de Sousa, VA}, title = {Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {9}, pages = {}, pmid = {37177482}, issn = {1424-8220}, mesh = {Humans ; *Imagery, Psychotherapy ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Machine Learning ; }, abstract = {Electroencephalography (EEG) is a fundamental tool for understanding the brain's electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.}, }
@article {pmid37177443, year = {2023}, author = {Ortega-Rodríguez, J and Gómez-González, JF and Pereda, E}, title = {Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {9}, pages = {}, pmid = {37177443}, issn = {1424-8220}, mesh = {Humans ; Electroencephalography/methods ; Brain ; *Biometric Identification ; Electrodes ; Biometry ; *Brain-Computer Interfaces ; }, abstract = {Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain-machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.}, }
@article {pmid37181286, year = {2022}, author = {Khrulev, AE and Kuryatnikova, KM and Belova, &#x410;N and Popova, PS and Khrulev, S&#x415;}, title = {Modern Rehabilitation Technologies of Patients with Motor Disorders at an Early Rehabilitation of Stroke (Review).}, journal = {Sovremennye tekhnologii v meditsine}, volume = {14}, number = {6}, pages = {64-78}, pmid = {37181286}, issn = {2309-995X}, mesh = {Adult ; Humans ; *Stroke Rehabilitation/methods ; *Transcranial Direct Current Stimulation/methods ; *Motor Disorders ; Recovery of Function ; *Stroke ; }, abstract = {Cerebral stroke is one of the leading disability causes among adult population worldwide. The number of post-stroke patients, who need rehabilitation including motor recovery, keeps growing annually. Standard motor rehabilitation techniques have a limited effect on recovering extremity motor defunctionalization. In this regard, in recent years, new technologies of post-stroke rehabilitation are being suggested. The present review summarizes the existing literature data on current techniques applied in patients with motor disorders at an early rehabilitation period of cerebral stroke. The current modern technologies are divided into the methods based on "interhemispheric inhibition" theory (repetitive transcranial magnetic stimulation, transcranial direct current stimulation), and on "mirror neurons" theory (virtual reality systems and brain-computer interfaces). The authors present the neurophysiological causes and feasible protocols of using the techniques in clinical practice, the clinical research findings due to the initial severity level of motor disorders and stroke age, as well as the factors contributing to the motor rehabilitation efficiency when using these methods.}, }
@article {pmid37172612, year = {2023}, author = {Saini, M and Satija, U}, title = {State of art mental tasks classification based on electroencephalogram: a review.}, journal = {Physiological measurement}, volume = {}, number = {}, pages = {}, doi = {10.1088/1361-6579/acd51b}, pmid = {37172612}, issn = {1361-6579}, abstract = {Electroencephalogram (EEG) plays an important role in analyzing different mental tasks and neurological disorders. Hence, it is a critical component for designing various applications, such as brain-computer interfaces, neurofeedback, etc. Mental task classification (MTC) is one of the research focuses in these applications. Therefore, numerous MTC techniques have been proposed in literary works. Although various literature reviews exist based on EEG signals for different neurological disorders and behavior analysis, there is a lack of a review of state-of-the-art MTC techniques. Therefore, this paper presents a detailed review for the MTC techniques including the classification of mental tasks and mental workload. A brief description of EEG along with its physiological and non-physiological artifacts is also presented. Further, we include information on several publicly available databases, features, classifiers, and performance metrics used in MTC studies. We implement and evaluate some of the commonly used existing MTC techniques in the presence of different artifacts and subjects, based on which the challenges and directions are highlighted for future research in MTC.}, }
@article {pmid37172446, year = {2023}, author = {Zhang, F and Yang, Y and Xin, Y and Sun, Y and Wang, C and Zhu, J and Tang, T and Zhang, J and Xu, K}, title = {Efficacy of different strategies of responsive neurostimulation on seizure control and their association with acute neurophysiological effects in rats.}, journal = {Epilepsy &amp; behavior : E&amp;B}, volume = {143}, number = {}, pages = {109212}, doi = {10.1016/j.yebeh.2023.109212}, pmid = {37172446}, issn = {1525-5069}, abstract = {Responsive neurostimulation (RNS) has shown promising but limited efficacy in the treatment of drug-resistant epilepsy. The clinical utility of RNS is hindered by the incomplete understanding of the mechanism behind its therapeutic effects. Thus, assessing the acute effects of responsive stimulation (AERS) based on intracranial EEG recordings in the temporal lobe epilepsy rat model may provide a better understanding of the potential therapeutic mechanisms underlying the antiepileptic effect of RNS. Furthermore, clarifying the correlation between AERS and seizure severity may help guide the optimization of RNS parameter settings. In this study, RNS with high (130 Hz) and low frequencies (5 Hz) was applied to the subiculum (SUB) and CA1. To quantify the changes induced by RNS, we calculated the AERS during synchronization by Granger causality and analyzed the band power ratio in the classic power band after different stimulations were delivered in the interictal and seizure onset periods, respectively. This demonstrates that only targets combined with an appropriate stimulation frequency could be efficient for seizure control. High-frequency stimulation of CA1 significantly shortened the ongoing seizure duration, which may be causally related to increased synchronization after stimulation. Both high-frequency stimulation of the CA1 and low-frequency stimulation delivered to the SUB reduced seizure frequency, and the reduced seizure risk may correlate with the change in power ratio near the theta band. It indicated that different stimulations may control seizures in diverse manners, perhaps with disparate mechanisms. More focus should be placed on understanding the correlation between seizure severity and synchronization and rhythm around theta bands to simplify the process of parameter optimization.}, }
@article {pmid37171929, year = {2023}, author = {Wang, Z and Yang, L and Wang, M and Zhou, Y and Chen, L and Gu, B and Liu, S and Xu, M and He, F and Ming, D}, title = {Motor imagery and action observation induced electroencephalographic activations to guide subject-specific training paradigm: a pilot study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3275572}, pmid = {37171929}, issn = {1558-0210}, abstract = {Brain-computer interface (BCI)-based motor rehabilitation feedback training system can facilitate motor function reconstruction, but its rehabilitation mechanism with suitable training protocol is unclear, which affects the application effect. To this end, we probed the electroencephalographic (EEG) activations induced by motor imagery (MI) and action observation (AO) to provide an effective method to optimize motor feedback training. We grouped subjects according to their alpha-band sensorimotor cortical excitability under MI and AO conditions, and investigated the EEG response under the same paradigm between groups and different motor paradigms within group, respectively. The results showed that there were significant differences in sensorimotor activations between two groups of subjects. Specifically, the group with weaker MI induced EEG features, could achieve stronger sensorimotor activations in AO than that of other conditions. The group with stronger MI induced EEG features, could achieve stronger sensorimotor activations in the MI+AO than that of other conditions. We also explored their classification and brain network differences, which might try to explain the EEG mechanism in different individuals and help stroke patients to choose appropriate subject-specific motor training paradigm for their rehabilitation and better treatment outcomes.}, }
@article {pmid37171927, year = {2023}, author = {Zhao, J and Shi, Y and Liu, W and Zhou, T and Li, Z and Li, X}, title = {A Hybrid Method Fusing Frequency Recognition with Attention Detection to Enhance an Asynchronous Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3275547}, pmid = {37171927}, issn = {1558-0210}, abstract = {OBJECTIVE: One critical problem in controlling an asynchronous brain-computer interface (BCI) system is to discriminate between control and idle states. This paper proposes a hybrid attention detection and frequency recognition method based on weighted Dempster-Shafer theory (ADFR-DS), which integrates information of different aspects of the task from two brain regions, to enhance asynchronous control performance of a steady-state visual evoked potential (SSVEP)-based BCI system.<br><br>METHODS: The ADFR-DS method utilizes a hybrid architecture to process electroencephalogram (EEG) data from different channels simultaneously: an individualized frequency band based optimized complex network (IFBOCN) algorithm processes neural activity from the prefrontal area for attention detection, and an ensemble task-related component analysis (eTRCA) algorithm processes data from the occipital area for frequency recognition. The ADFR-DS method then fuses their classification results at decision level to generate the final output of the BCI system. A novel weighted Dempster-Shafer fusion method was proposed to enhance the fusion performance. This study evaluated the proposed method using a 40-target dataset recorded from 35 participants.<br><br>MAIN RESULTS: The proposed method outperformed the eTRCA algorithm in the true positive rate (TPR), true negative rate (TNR), accuracy (ACC) and information transfer rate (ITR). Specifically, ADFR-DS improved the average ACC of eTRCA from 62.71% to 69.30%, and improved the average ITR from 184.28 bits/min to 216.89 bits/min (data length 0.3 s).<br><br>CONCLUSION: The results suggest that the proposed ADFR-DS method can enhance asynchronous SSVEP-based BCI systems.}, }
@article {pmid37170160, year = {2022}, author = {Li, M and Cheng, S and Fan, J and Shang, Z and Wan, H and Yang, L and Yang, L}, title = {Disarrangement and reorganization of the hippocampal functional connectivity during the spatial path adjustment of pigeons.}, journal = {BMC zoology}, volume = {7}, number = {1}, pages = {54}, pmid = {37170160}, issn = {2056-3132}, abstract = {BACKGROUND: The hippocampus plays an important role to support path planning and adjustment in goal-directed spatial navigation. While we still only have limited knowledge about how do the hippocampal neural activities, especially the functional connectivity patterns, change during the spatial path adjustment. In this study, we measured the behavioural indicators and local field potentials of the pigeon (Columba livia, male and female) during a goal-directed navigational task with the detour paradigm, exploring the changing patterns of the hippocampal functional network connectivity of the bird during the spatial path learning and adjustment.<br><br>RESULTS: Our study demonstrates that the pigeons progressively learned to solve the path adjustment task after the preferred path is blocked suddenly. Behavioural results show that both the total duration and the path lengths pigeons completed the task during the phase of adjustment are significantly longer than those during the acquisition and recovery phases. Furthermore, neural results show that hippocampal functional connectivity selectively changed during path adjustment. Specifically, we identified depressed connectivity in lower bands (delta and theta) and elevated connectivity in higher bands (slow-gamma and fast-gamma).<br><br>CONCLUSIONS: These results feature both the behavioural response and neural representation of the avian spatial cognitive learning process, suggesting that the functional disarrangement and reorganization of the connectivity in the avian hippocampus during different phases may contribute to our further understanding of the potential mechanism of path learning and adjustment.}, }
@article {pmid37169016, year = {2023}, author = {Sun, H and Li, C and Zhang, H}, title = {Design of virtual BCI channels based on informer.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1150316}, pmid = {37169016}, issn = {1662-5161}, abstract = {The precision and reliability of electroencephalogram (EEG) data are essential for the effective functioning of a brain-computer interface (BCI). As the number of BCI acquisition channels increases, more EEG information can be gathered. However, having too many channels will reduce the practicability of the BCI system, raise the likelihood of poor-quality channels, and lead to information misinterpretation. These issues pose challenges to the advancement of BCI systems. Determining the optimal configuration of BCI acquisition channels can minimize the number of channels utilized, but it is challenging to maintain the original operating system and accommodate individual variations in channel layout. To address these concerns, this study introduces the EEG-completion-informer (EC-informer), which is based on the Informer architecture known for its effectiveness in time-series problems. By providing input from four BCI acquisition channels, the EC-informer can generate several virtual acquisition channels to extract additional EEG information for analysis. This approach allows for the direct inheritance of the original model, significantly reducing researchers' workload. Moreover, EC-informers demonstrate strong performance in damaged channel repair and poor channel identification. Using the Informer as a foundation, the study proposes the EC-informer, tailored to BCI requirements and demanding only a small number of training samples. This approach eliminates the need for extensive computing units to train an efficient, lightweight model while preserving comprehensive information about target channels. The study also confirms that the proposed model can be transferred to other operators with minimal loss, exhibiting robust applicability. The EC-informer's features enable original BCI devices to adapt to a broader range of classification algorithms and relax the operational requirements of BCI devices, which could facilitate the promotion of the use of BCI devices in daily life.}, }
@article {pmid37168809, year = {2023}, author = {Albahri, AS and Al-Qaysi, ZT and Alzubaidi, L and Alnoor, A and Albahri, OS and Alamoodi, AH and Bakar, AA}, title = {A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.}, journal = {International journal of telemedicine and applications}, volume = {2023}, number = {}, pages = {7741735}, pmid = {37168809}, issn = {1687-6415}, abstract = {The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.}, }
@article {pmid37167975, year = {2023}, author = {Cao, K and Qiu, L and Lu, X and Wu, W and Hu, Y and Cui, Z and Jiang, C and Luo, Y and Shao, Y and Xi, W and Zeng, LH and Xu, H and Ma, H and Zhang, Z and Peng, J and Duan, S and Gao, Z}, title = {Microglia modulate general anesthesia through P2Y12 receptor.}, journal = {Current biology : CB}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cub.2023.04.047}, pmid = {37167975}, issn = {1879-0445}, abstract = {General anesthesia (GA) is an unconscious state produced by anesthetic drugs, which act on neurons to cause overall suppression of neuronal activity in the brain. Recent studies have revealed that GA also substantially enhances the dynamics of microglia, the primary brain immune cells, with increased process motility and territory surveillance. However, whether microglia are actively involved in GA modulation remains unknown. Here, we report a previously unrecognized role for microglia engaging in multiple GA processes. We found that microglial ablation reduced the sensitivity of mice to anesthetics and substantially shortened duration of loss of righting reflex (LORR) or unconsciousness induced by multiple anesthetics, thereby promoting earlier emergence from GA. Microglial repopulation restored the regular anesthetic recovery, and chemogenetic activation of microglia prolonged the duration of LORR. In addition, anesthesia-accompanying analgesia and hypothermia were also attenuated after microglial depletion. Single-cell RNA sequencing analyses showed that anesthesia prominently affected the transcriptional levels of chemotaxis and migration-related genes in microglia. By pharmacologically targeting different microglial motility pathways, we found that blocking P2Y12 receptor (P2Y12R) reduced the duration of LORR of mice. Moreover, genetic ablation of P2Y12R in microglia also promoted quicker recovery in mice from anesthesia, verifying the importance of microglial P2Y12R in anesthetic regulation. Our work presents the first evidence that microglia actively participate in multiple processes of GA through P2Y12R-mediated signaling and expands the non-immune roles of microglia in the brain.}, }
@article {pmid37168652, year = {2022}, author = {Shi, Y and Ananthakrishnan, A and Oh, S and Liu, X and Hota, G and Cauwenberghs, G and Kuzum, D}, title = {A Neuromorphic Brain Interface based on RRAM Crossbar Arrays for High Throughput Real-time Spike Sorting.}, journal = {IEEE transactions on electron devices}, volume = {69}, number = {4}, pages = {2137-2144}, pmid = {37168652}, issn = {0018-9383}, support = {DP2 EB030992/EB/NIBIB NIH HHS/United States ; }, abstract = {Real-time spike sorting and processing are crucial for closed-loop brain-machine interfaces and neural prosthetics. Recent developments in high-density multi-electrode arrays with hundreds of electrodes have enabled simultaneous recordings of spikes from a large number of neurons. However, the high channel count imposes stringent demands on real-time spike sorting hardware regarding data transmission bandwidth and computation complexity. Thus, it is necessary to develop a specialized real-time hardware that can sort neural spikes on the fly with high throughputs while consuming minimal power. Here, we present a real-time, low latency spike sorting processor that utilizes high-density CuOx resistive crossbars to implement in-memory spike sorting in a massively parallel manner. We developed a fabrication process which is compatible with CMOS BEOL integration. We extensively characterized switching characteristics and statistical variations of the CuOx memory devices. In order to implement spike sorting with crossbar arrays, we developed a template matching-based spike sorting algorithm that can be directly mapped onto RRAM crossbars. By using synthetic and in vivo recordings of extracellular spikes, we experimentally demonstrated energy efficient spike sorting with high accuracy. Our neuromorphic interface offers substantial improvements in area (~1000× less area), power (~200× less power), and latency (4.8μs latency for sorting 100 channels) for real-time spike sorting compared to other hardware implementations based on FPGAs and microcontrollers.}, }
@article {pmid37167876, year = {2023}, author = {Zhou, L and Xu, Y and Song, F and Li, W and Gao, F and Zhu, Q and Qian, Z}, title = {The effect of TENS on sleep: A pilot study.}, journal = {Sleep medicine}, volume = {107}, number = {}, pages = {126-136}, doi = {10.1016/j.sleep.2023.04.029}, pmid = {37167876}, issn = {1878-5506}, abstract = {BACKGROUND: Insomnia is the second most common neuropsychiatric disorder, but the current treatments are not very effective. There is therefore an urgent need to develop better treatments. Transcutaneous electrical nerve stimulation (TENS) may be a promising means of treating insomnia.<br><br>OBJECTIVE: This work aims to explore whether and how TENS modulate sleep and the effect of stimulation waveforms on sleep.<br><br>METHODS: Forty-five healthy subjects participated in this study. Electroencephalography (EEG) data were recorded before and after four mode low-frequency (1&#xa0;Hz) TENS with different waveforms, which were formed by superimposing sine waves of different high frequencies (60-210&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). The four waveform modes are formed by combining sine waves of varying frequencies. Mode 1 (M1) consists of a combination of high frequencies (60-110&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). Mode 2 (M2) is made up of high frequencies (60-210&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). Mode 3 (M3) consists of high frequencies (110-160&#xa0;Hz) and low frequencies (1-6&#xa0;Hz), while mode 4 (M4) is composed of high frequencies (160-210&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). For M1, M3 and M4, the high frequency portions of the stimulus waveforms account for 50%, while for M2, the high frequency portion of the waveform accounts for 65%. For each mode, the current intensities ranged from 4&#xa0;mA to 7&#xa0;mA, with values for each participant adjusted according to individual tolerance. During stimulation, the subjects were stimulated at the greater occipital nerve by the four mode TENS.<br><br>RESULTS: M1, M3, and M4 slowed down the frequency of neural activity, broadened the distribution of theta waves, and caused a decrease in activity in wakefulness-related regions and an increase in activity in sleep-related regions. However, M2 has the opposite modulation effect.<br><br>CONCLUSION: These results indicated that low-frequency TENS (1&#xa0;Hz) may facilitate sleep in a waveform-specific manner. Our findings provide new insights into the mechanisms of sleep modulation by TENS and the design of effective insomnia treatments.}, }
@article {pmid37167054, year = {2023}, author = {Wang, P and Li, Z and Gong, P and Zhou, Y and Chen, F and Zhang, D}, title = {MTRT: Motion Trajectory Reconstruction Transformer for EEG-based BCI Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3275172}, pmid = {37167054}, issn = {1558-0210}, abstract = {Brain computer interface (BCI) is a system that directly uses brain neural activities to communicate with the outside world. Recently, the decoding of the human upper limb based on electroencephalogram (EEG) signals has become an important research branch of BCI. Even though existing research models are capable of decoding upper limb trajectories, the performance needs to be improved to make them more practical for real-world applications. This study is attempt to reconstruct the continuous and nonlinear multi-directional upper limb trajectory based on Chinese sign language. Here, to reconstruct the upper limb motion trajectory effectively, we propose a novel Motion Trajectory Reconstruction Transformer (MTRT) neural network that utilizes the geometric information of human joint points and EEG neural activity signals to decode the upper limb trajectory. Specifically, we use human upper limb bone geometry properties as reconstruction constraints to obtain more accurate trajectory information of the human upper limbs. Furthermore, we propose a MTRT neural network based on this constraint, which uses the shoulder, elbow, and wrist joint point information and EEG signals of brain neural activity during upper limb movement to train its parameters. To validate the model, we collected the synchronization information of EEG signals and upper limb motion joint points of 20 subjects. The experimental results show that the reconstruction model can accurately reconstruct the motion trajectory of the shoulder, elbow, and wrist of the upper limb, achieving superior performance than the compared methods. This research is very meaningful to decode the limb motion parameters for BCI, and it is inspiring for the motion decoding of other limbs and other joints.}, }
@article {pmid37166297, year = {2023}, author = {Padfield, N and Agius Anastasi, A and Camilleri, T and Fabri, S and Bugeja, M and Camilleri, K}, title = {BCI-controlled wheelchairs: end-users' perceptions, needs, and expectations, an interview-based study.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/17483107.2023.2211602}, pmid = {37166297}, issn = {1748-3115}, abstract = {PURPOSE: Brain-computer interface (BCI)-controlled wheelchairs have the potential to improve the independence of people with mobility impairments. The low uptake of BCI devices has been linked to a lack of knowledge among researchers of the needs of end-users that should influence BCI development.<br><br>MATERIALS AND METHODS: This study used semi-structured interviews to learn about the perceptions, needs, and expectations of spinal cord injury (SCI) patients with regards to a BCI-controlled wheelchair. Topics discussed in the interview include: paradigms, shared control, safety, robustness, channel selection, hardware, and experimental design. The interviews were recorded and then transcribed. Analysis was carried out using coding based on grounded theory principles.<br><br>RESULTS: The majority of participants had a positive view of BCI-controlled wheelchair technology and were willing to use the technology. Core issues were raised regarding safety, cost and aesthetics. Interview discussions were linked to state-of-the-art BCI technology. The results challenge the current reliance of researchers on the motor-imagery paradigm by suggesting end-users expect highly intuitive paradigms. There also needs to be a stronger focus on obstacle avoidance and safety features in BCI wheelchairs. Finally, the development of control approaches that can be personalized for individual users may be instrumental for widespread adoption of these devices.<br><br>CONCLUSIONS: This study, based on interviews with SCI patients, indicates that BCI-controlled wheelchairs are a promising assistive technology that would be well received by end-users. Recommendations for a more person-centered design of BCI controlled wheelchairs are made and clear avenues for future research are identified.IMPLICATIONS FOR REHABILITATIONBrain-computer interface (BCI)-controlled wheelchairs are a promising assistive technology. The majority of participants had positive views of these devices and showed a willingness to try out such a device.Concerns centered on safety, cost and aesthetics.Integrated obstacle avoidance was viewed positively by most of the participants, but some had a negative view, expressing concerns about its safety, or reduced autonomy. Customizable control options should thus be integrated to cater for the needs of different individuals.}, }
@article {pmid37163609, year = {2023}, author = {Song, S and Fallegger, F and Trouillet, A and Kim, K and Lacour, SP}, title = {Deployment of an electrocorticography system with a soft robotic actuator.}, journal = {Science robotics}, volume = {8}, number = {78}, pages = {eadd1002}, doi = {10.1126/scirobotics.add1002}, pmid = {37163609}, issn = {2470-9476}, abstract = {Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery and increasingly explored in brain-machine interface applications. Current devices display limitations that require trade-offs among cortical surface coverage, spatial electrode resolution, aesthetic, and risk consequences and often limit the use of the mapping technology to the operating room. In this work, we report on a scalable technique for the fabrication of large-area soft robotic electrode arrays and their deployment on the cortex through a square-centimeter burr hole using a pressure-driven actuation mechanism called eversion. The deployable system consists of up to six prefolded soft legs, and it is placed subdurally on the cortex using an aqueous pressurized solution and secured to the pedestal on the rim of the small craniotomy. Each leg contains soft, microfabricated electrodes and strain sensors for real-time deployment monitoring. In a proof-of-concept acute surgery, a soft robotic electrode array was successfully deployed on the cortex of a minipig to record sensory cortical activity. This soft robotic neurotechnology opens promising avenues for minimally invasive cortical surgery and applications related to neurological disorders such as motor and sensory deficits.}, }
@article {pmid37162904, year = {2023}, author = {Herring, EZ and Graczyk, EL and Memberg, WD and Adams, RD and Baca-Vaca, GF and Hutchison, BC and Krall, JT and Alexander, BJ and Conlan, EC and Alfaro, KE and Bhat, PR and Ketting-Olivier, AB and Haddix, CA and Taylor, DM and Tyler, DJ and Kirsch, RF and Ajiboye, AB and Miller, JP}, title = {Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration after Tetraplegia.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.04.24.23288977}, pmid = {37162904}, abstract = {BACKGROUND: Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia.<br><br>OBJECTIVE: We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation.<br><br>METHODS: A right-handed man with motor-complete C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of a bidirectional brain-machine interface to restore function of the participant's own arm and hand.<br><br>RESULTS: Multi-unit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions. The system was well tolerated with no operative complications.<br><br>CONCLUSION: The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to allow restoration of motor and sensory functions of the arm and hand after neurological injury.}, }
@article {pmid37161677, year = {2023}, author = {Shen, Q and Fu, S and Jiang, X and Huang, X and Lin, D and Xiao, Q and Khadijah, S and Yan, Y and Xiong, X and Jin, J and Ebstein, RP and Xu, T and Wang, Y and Feng, J}, title = {Factual and counterfactual learning in major adolescent depressive disorder, evidence from an instrumental learning study.}, journal = {Psychological medicine}, volume = {}, number = {}, pages = {1-11}, doi = {10.1017/S0033291723001307}, pmid = {37161677}, issn = {1469-8978}, abstract = {BACKGROUND: The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.<br><br>METHODS: We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).<br><br>RESULTS: The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.<br><br>CONCLUSIONS: By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.}, }
@article {pmid37160488, year = {2023}, author = {Emigh, B and Grigorian, A and Dilday, J and Condon, F and Nahmias, J and Schellenberg, M and Martin, M and Matsushima, K and Inaba, K}, title = {Risk factors and outcomes in pediatric blunt cardiac injuries.}, journal = {Pediatric surgery international}, volume = {39}, number = {1}, pages = {195}, pmid = {37160488}, issn = {1437-9813}, mesh = {Adolescent ; Adult ; Humans ; Child ; Hemothorax ; Risk Factors ; *Contusions ; *Wounds, Nonpenetrating/epidemiology ; *Myocardial Contusions ; }, abstract = {PURPOSE: Unlike adults, less is known of the etiology and risk factors for blunt cardiac injury (BCI) in children. Identifying risk factors for BCI in pediatric patients will allow for more specific screening practices following blunt trauma.<br><br>METHODS: A retrospective review was performed using the Trauma Quality Improvement Program (TQIP) database from 2017 to 2019. All patients &#x2264;&#x2009;16&#xa0;years injured following blunt trauma were included. Demographics, mechanism, associated injuries, injury severity, and outcomes were collected. Univariate and multivariate regression was used to determine specific risk factors for BCI.<br><br>RESULTS: Of 266,045 pediatric patients included in the analysis, the incidence of BCI was less than 0.2%. The all-cause mortality seen in patients with BCI was 26%. Motor-vehicle collisions (MVCs) were the most common mechanism, although no association with seatbelt use was seen in adolescents (p&#x2009;=&#x2009;0.158). The strongest independent risk factors for BCI were pulmonary contusions (OR 15.4, p&#x2009;<&#x2009;0.001) and hemothorax (OR 8.9, p&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: Following trauma, the presence of pulmonary contusions or hemothorax should trigger additional screening investigations specific for BCI in pediatric patients.}, }
@article {pmid37160127, year = {2023}, author = {Guan, C and Aflalo, TN and Kadlec, K and Gámez de Leon, J and Rosario, ER and Bari, A and Pouratian, N and Andersen, RA}, title = {Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acd3b1}, pmid = {37160127}, issn = {1741-2552}, abstract = {Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis. Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis (LDA) with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands. Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands. Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.}, }
@article {pmid37158939, year = {2023}, author = {Wang, J and Yin, C and Pan, Y and Yang, Y and Li, W and Ni, H and Liu, B and Nie, H and Xu, R and Wei, H and Zhang, Y and Li, Y and Hu, Q and Tai, Y and Shao, X and Fang, J and Liu, B}, title = {CXCL13 contributes to chronic pain of a mouse model of CRPS-I via CXCR5-mediated NF-κB activation and pro-inflammatory cytokine production in spinal cord dorsal horn.}, journal = {Journal of neuroinflammation}, volume = {20}, number = {1}, pages = {109}, pmid = {37158939}, issn = {1742-2094}, mesh = {Animals ; Mice ; *Chronic Pain ; NF-kappa B ; Chemokine CXCL13 ; Hyperalgesia ; Neuroinflammatory Diseases ; *Reflex Sympathetic Dystrophy ; Spinal Cord Dorsal Horn ; Disease Models, Animal ; }, abstract = {BACKGROUND: Complex regional pain syndrome type-I (CRPS-I) causes excruciating pain that affect patients' life quality. However, the mechanisms underlying CRPS-I are incompletely understood, which hampers the development of target specific therapeutics.<br><br>METHODS: The mouse chronic post-ischemic pain (CPIP) model&#xa0;was established to mimic CRPS-I. qPCR, Western blot, immunostaining, behavioral assay and pharmacological methods were used to study mechanisms underlying neuroinflammation and chronic pain in spinal cord dorsal horn (SCDH) of CPIP mice.<br><br>RESULTS: CPIP mice developed robust and long-lasting mechanical allodynia in bilateral hindpaws. The expression of inflammatory chemokine CXCL13 and its receptor CXCR5 was significantly upregulated in ipsilateral SCDH of CPIP mice. Immunostaining revealed CXCL13 and CXCR5 was predominantly expressed in spinal neurons. Neutralization of spinal CXCL13 or genetic deletion of Cxcr5 (Cxcr5[-/-]) significantly reduced mechanical allodynia, as well as spinal glial cell overactivation and c-Fos activation in SCDH of CPIP mice. Mechanical pain causes affective disorder in CPIP mice, which was attenuated in Cxcr5[-/-] mice. Phosphorylated STAT3 co-expressed with CXCL13 in SCDH neurons and contributed to CXCL13 upregulation and mechanical allodynia in CPIP mice. CXCR5 coupled with NF-&#x3ba;B signaling in SCDH neurons to trigger pro-inflammatory cytokine gene Il6 upregulation, contributing to mechanical allodynia. Intrathecal CXCL13 injection produced mechanical allodynia via CXCR5-dependent NF-&#x3ba;B activation. Specific overexpression of CXCL13 in SCDH neurons is sufficient to induce persistent mechanical allodynia in na&#xef;ve mice.<br><br>CONCLUSIONS: These results demonstrated a previously unidentified role of CXCL13/CXCR5 signaling in mediating spinal neuroinflammation and mechanical pain in an animal model of CRPS-I. Our work suggests that targeting CXCL13/CXCR5 pathway may lead to novel therapeutic approaches for CRPS-I.}, }
@article {pmid37158916, year = {2023}, author = {Su, W and Ju, J and Gu, M and Wang, X and Liu, S and Yu, J and Mu, D}, title = {SARS-CoV-2 envelope protein triggers depression-like behaviors and dysosmia via TLR2-mediated neuroinflammation in mice.}, journal = {Journal of neuroinflammation}, volume = {20}, number = {1}, pages = {110}, pmid = {37158916}, issn = {1742-2094}, mesh = {Female ; Male ; Animals ; Mice ; *COVID-19 ; Depression/etiology ; Interleukin-6 ; Neuroinflammatory Diseases ; SARS-CoV-2 ; Toll-Like Receptor 2 ; *Olfaction Disorders/etiology ; }, abstract = {BACKGROUND: Depression and dysosmia have been regarded as primary neurological symptoms in COVID-19 patients, the mechanism of which remains unclear. Current studies have demonstrated that the SARS-CoV-2 envelope (E) protein is a pro-inflammatory factor sensed by Toll-like receptor 2 (TLR2), suggesting the pathological feature of E protein is independent of viral infection. In this study, we aim to ascertain the role of E protein in depression, dysosmia and associated neuroinflammation in the central nervous system (CNS).<br><br>METHODS: Depression-like behaviors and olfactory function were observed in both female and male mice receiving intracisternal injection of E protein. Immunohistochemistry was applied in conjunction with RT-PCR to evaluate glial activation, blood-brain barrier status and mediators synthesis in the cortex, hippocampus and olfactory bulb. TLR2 was pharmacologically blocked to determine its role in E protein-related depression-like behaviors and dysosmia in mice.<br><br>RESULTS: Intracisternal injection of E protein evoked depression-like behaviors and dysosmia in both female and male mice. Immunohistochemistry suggested that the E protein upregulated IBA1 and GFAP in the cortex, hippocampus and olfactory bulb, while ZO-1 was downregulated. Moreover, IL-1&#x3b2;, TNF-&#x3b1;, IL-6, CCL2, MMP2 and CSF1 were upregulated in both cortex and hippocampus, whereas IL-1&#x3b2;, IL-6 and CCL2 were upregulated in the olfactory bulb. Furtherly, inhibiting microglia, rather than astrocytes, alleviated depression-like behaviors and dysosmia induced by E protein. Finally, RT-PCR and immunohistochemistry suggested that TLR2 was upregulated in the cortex, hippocampus and olfactory bulb, the blocking of which mitigated depression-like behaviors and dysosmia induced by E protein.<br><br>CONCLUSIONS: Our study demonstrates that envelope protein could directly induce depression-like behaviors, dysosmia, and obvious neuroinflammation in CNS. TLR2 mediated depression-like behaviors and dysosmia induced by envelope protein, which could serve as a promising therapeutic target for neurological manifestation in COVID-19 patients.}, }
@article {pmid37155399, year = {2023}, author = {Li, H and Xu, G and Li, Z and Zhang, K and Zheng, X and Du, C and Han, C and Kuang, J and Du, Y and Zhang, S}, title = {A Precise Frequency Recognition Method of Short-time SSVEP Signals Based on Signal Extension.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3274121}, pmid = {37155399}, issn = {1558-0210}, abstract = {OBJECTIVE: Improving the Information Transfer Rate (ITR) is a popular research topic in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The higher recognition accuracy of short-time SSVEP signal is critical to improving ITR and achieving high-speed SSVEP-BCIs. However, the existing algorithms have unsatisfactory performance on recognizing short-time SSVEP signals, especially for calibration-free methods.<br><br>METHOD: This study for the first time proposed improving the recognition accuracy of short-time SSVEP signals based on the calibration-free method by extending the SSVEP signal length. A signal extension model based on Multi-channel adaptive Fourier decomposition with differ-ent Phase (DP-MAFD) is proposed to achieve signal exten-sion. Then the Canonical Correlation Analysis based on signal extension (SE-CCA) is proposed to complete the recognition and classification of SSVEP signals after exten-sion.<br><br>RESULT: The similarity study and SNR comparison analysis on public SSVEP datasets demonstrate that the proposed signal extension model has the ability to extend SSVEP signals. The classification results show that the pro-posed method outperforms Canonical Correlation Analysis (CCA) and Filter Bank Canonical Correlation Analysis (FBCCA) significantly in the measure of classification accu-racy and information transmission rate (ITR), especially for short-time signals. The highest ITR of SE-CCA is improved to 175.61 bits/min at around 1s, while CCA is 100.55 bits/min at 1.75s and FBCCA is 141.76 bits/min at 1.25s.<br><br>CONCLUSION: The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.}, }
@article {pmid37152432, year = {2023}, author = {Wu, C and Shang, HF and Wang, YJ and Wang, JH and Zuo, ZX and Lian, YN and Liu, L and Zhang, C and Li, XY}, title = {Cingulate protein arginine methyltransferases 1 regulates peripheral hypersensitivity via fragile X messenger ribonucleoprotein.}, journal = {Frontiers in molecular neuroscience}, volume = {16}, number = {}, pages = {1153870}, pmid = {37152432}, issn = {1662-5099}, abstract = {The deficit of fragile X messenger ribonucleoprotein (FMRP) leads to intellectual disability in human and animal models, which also leads to desensitization of pain after nerve injury. Recently, it was shown that the protein arginine methyltransferases 1 (PRMT1) regulates the phase separation of FMRP. However, the role of PRMT1 in pain regulation has been less investigated. Here we showed that the downregulation of PRMT1 in the anterior cingulate cortex (ACC) contributes to the development of peripheral pain hypersensitivity. We observed that the peripheral nerve injury decreased the expression of PRMT1 in the ACC; knockdown of the PRMT1 via shRNA in the ACC decreased the paw withdrawal thresholds (PWTs) of naïve mice. Moreover, the deficits of FMRP abolished the effects of PRMT1 on pain sensation. Furthermore, overexpression of PRMT1 in the ACC increased the PWTs of mice with nerve injury. These observations indicate that the downregulation of cingulate PRMT1 was necessary and sufficient to develop peripheral hypersensitivity after nerve injury. Thus, we provided evidence that PRMT1 is vital in regulating peripheral pain hypersensitivity after nerve injury via the FMRP.}, }
@article {pmid37149621, year = {2023}, author = {Catalán, JM and Trigili, E and Nann, M and Blanco-Ivorra, A and Lauretti, C and Cordella, F and Ivorra, E and Armstrong, E and Crea, S and Alcañiz, M and Zollo, L and Soekadar, SR and Vitiello, N and García-Aracil, N}, title = {Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs).}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {61}, pmid = {37149621}, issn = {1743-0003}, mesh = {Male ; Female ; Humans ; Adult ; Middle Aged ; Aged ; *Activities of Daily Living ; *Exoskeleton Device ; Quality of Life ; Reproducibility of Results ; Brain ; }, abstract = {BACKGROUND: The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator.<br><br>METHODS: Ten impaired participants (5 males and 5 females, mean age 52 &#xb1; 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3&#x2009;s and a reliable control in case it remained below 5&#x2009;s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability.<br><br>RESULTS: All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3&#x2009;s in case of the exoskeleton and bellow 5s in case of the external manipulator).<br><br>CONCLUSIONS: Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.}, }
@article {pmid37148587, year = {2023}, author = {King, BJ and Read, GJM and Salmon, PM}, title = {Identifying risk controls for future advanced brain-computer interfaces: A prospective risk assessment approach using work domain analysis.}, journal = {Applied ergonomics}, volume = {111}, number = {}, pages = {104028}, doi = {10.1016/j.apergo.2023.104028}, pmid = {37148587}, issn = {1872-9126}, abstract = {Brain-computer interface (BCI) technologies are progressing rapidly and may eventually be implemented widely within society, yet their risks have arguably not yet been comprehensively identified, nor understood. This study analysed an anticipated invasive BCI system lifecycle to identify the individual, organisational, and societal risks associated with BCIs, and controls that could be used to mitigate or eliminate these risks. A BCI system lifecycle work domain analysis model was developed and validated with 10 subject matter experts. The model was subsequently used to undertake a systems thinking-based risk assessment approach to identify risks that could emerge when functions are either undertaken sub-optimally or not undertaken at all. Eighteen broad risk themes were identified that could negatively impact the BCI system lifecycle in a variety of unique ways, while a larger number of controls for these risks were also identified. The most concerning risks included inadequate regulation of BCI technologies and inadequate training of BCI stakeholders, such as users and clinicians. In addition to specifying a practical set of risk controls to inform BCI device design, manufacture, adoption, and utilisation, the results demonstrate the complexity involved in managing BCI risks and suggests that a system-wide coordinated response is required. Future research is required to evaluate the comprehensiveness of the identified risks and the practicality of implementing the risk controls.}, }
@article {pmid37148558, year = {2023}, author = {Li, XY and Bao, YF and Xie, JJ and Gao, B and Qian, SX and Dong, Y and Wu, ZY}, title = {Application Value of Serum Neurofilament Light Protein for Disease Staging in Huntington's Disease.}, journal = {Movement disorders : official journal of the Movement Disorder Society}, volume = {}, number = {}, pages = {}, doi = {10.1002/mds.29430}, pmid = {37148558}, issn = {1531-8257}, abstract = {BACKGROUND: Neurofilament light protein (NfL) has been proven to be a sensitive biomarker for Huntington's disease (HD). However, these studies did not include HD patients at advanced stages or with larger CAG repeats (>50), leading to a knowledge gap of the characteristics of NfL.<br><br>METHODS: Serum NfL (sNfL) levels were quantified using an ultrasensitive immunoassay. Participants were assessed by clinical scales and 7.0&#x2009;T magnetic resonance imaging. Longitudinal samples and clinical data were obtained.<br><br>RESULTS: Baseline samples were available from 110 controls, 90 premanifest HD (pre-HD) and 137 HD individuals. We found levels of sNfL significantly increased in HD compared to pre-HD and controls (both P&#x2009;<&#x2009;0.0001). The increase rates of sNfL were differed by CAG repeat lengths. However, there was no difference in sNfL levels in manifest HD from early to late stages. In addition, sNfL levels were associated with cognitive measures in pre-HD and manifest HD group, respectively. The increased levels of sNfL were also closely related to microstructural changes in white matter. In the longitudinal analysis, baseline sNfL did not correlate with subsequent clinical function decline. Random forest analysis revealed that sNfL had good power for predicting disease onset.<br><br>CONCLUSIONS: Although sNfL levels are independent of disease stages in manifest HD, it is still an optimal indicator for predicting disease onset and has potential use as a surrogate biomarker of treatment effect in clinical trials. &#xa9; 2023 International Parkinson and Movement Disorder Society.}, }
@article {pmid37148553, year = {2023}, author = {Neumann, WJ and Gilron, R and Little, S and Tinkhauser, G}, title = {Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation.}, journal = {Movement disorders : official journal of the Movement Disorder Society}, volume = {}, number = {}, pages = {}, doi = {10.1002/mds.29415}, pmid = {37148553}, issn = {1531-8257}, support = {K23NS120037/GF/NIH HHS/United States ; project number: PZ00P3_202166/SNSF_/Swiss National Science Foundation/Switzerland ; }, abstract = {Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.}, }
@article {pmid37147908, year = {2023}, author = {Ni, RJ and Wang, YY and Gao, TH and Wang, QR and Wei, JX and Zhao, LS and Ma, YR and Ma, XH and Li, T}, title = {Depletion of microglia with PLX3397 attenuates MK-801-induced hyperactivity associated with regulating inflammation-related genes in the brain.}, journal = {Zoological research}, volume = {44}, number = {3}, pages = {543-555}, doi = {10.24272/j.issn.2095-8137.2022.389}, pmid = {37147908}, issn = {2095-8137}, mesh = {Mice ; Animals ; *Dizocilpine Maleate/pharmacology/metabolism ; Microglia/metabolism ; Brain/metabolism ; *Inflammation/chemically induced/drug therapy/genetics/veterinary ; gamma-Aminobutyric Acid/metabolism ; Membrane Glycoproteins/metabolism ; Receptors, Immunologic/metabolism ; *Rodent Diseases/metabolism ; }, abstract = {Acute administration of MK-801 (dizocilpine), an N-methyl-D-aspartate receptor (NMDAR) antagonist, can establish animal models of psychiatric disorders. However, the roles of microglia and inflammation-related genes in these animal models of psychiatric disorders remain unknown. Here, we found rapid elimination of microglia in the prefrontal cortex (PFC) and hippocampus (HPC) of mice following administration of the dual colony-stimulating factor 1 receptor (CSF1R)/c-Kit kinase inhibitor PLX3397 (pexidartinib) in drinking water. Single administration of MK-801 induced hyperactivity in the open-field test (OFT). Importantly, PLX3397-induced depletion of microglia prevented the hyperactivity and schizophrenia-like behaviors induced by MK-801. However, neither repopulation of microglia nor inhibition of microglial activation by minocycline affected MK-801-induced hyperactivity. Importantly, microglial density in the PFC and HPC was significantly correlated with behavioral changes. In addition, common and distinct glutamate-, GABA-, and inflammation-related gene (116 genes) expression patterns were observed in the brains of PLX3397- and/or MK-801-treated mice. Moreover, 10 common inflammation-related genes (CD68, CD163, CD206, TMEM119, CSF3R, CX3CR1, TREM2, CD11b, CSF1R, and F4/80) with very strong correlations were identified in the brain using hierarchical clustering analysis. Further correlation analysis demonstrated that the behavioral changes in the OFT were most significantly associated with the expression of inflammation-related genes (NLRP3, CD163, CD206, F4/80, TMEM119, and TMEM176a), but not glutamate- or GABA-related genes in PLX3397- and MK-801-treated mice. Thus, our results suggest that microglial depletion via a CSF1R/c-Kit kinase inhibitor can ameliorate the hyperactivity induced by an NMDAR antagonist, which is associated with modulation of immune-related genes in the brain.}, }
@article {pmid37145943, year = {2023}, author = {Meng, L and Jiang, X and Huang, J and Zeng, Z and Yu, S and Jung, TP and Lin, CT and Chavarriaga, R and Wu, D}, title = {EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3273214}, pmid = {37145943}, issn = {1558-0210}, abstract = {Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it.}, }
@article {pmid37143288, year = {2023}, author = {Wang, J and Wang, T and Liu, H and Wang, K and Moses, K and Feng, Z and Li, P and Huang, W}, title = {Flexible Electrodes for Brain-Computer Interface System.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2211012}, doi = {10.1002/adma.202211012}, pmid = {37143288}, issn = {1521-4095}, abstract = {Brain-computer interface (BCI) has been the subject of extensive research recently. Governments and companies have substantially invested in relevant research and applications. The restoration of communication and motor function, the treatment of psychological disorders, gaming, and other daily and therapeutic applications all benefit from BCI. The electrodes hold the key to the essential, fundamental BCI precondition of electrical brain activity detection and delivery. However, the traditional rigid electrodes are limited due to their mismatch in Young's modulus, potential damages to the human body and a decline in signal quality with time. These factors make the development of flexible electrodes vital and urgent. Flexible electrodes made of soft materials have grown in popularity in recent years as an alternative to conventional rigid electrodes because they offer greater conformance, the potential for higher signal-to-noise ratio (SNR) signals, and a wider range of applications. Therefore, the latest classifications and future developmental directions of fabricating these flexible electrodes are explored in this paper to further encourage the speedy advent of flexible electrodes for BCI. In summary, the perspectives and future outlook for this developing discipline are provided. This article is protected by copyright. All rights reserved.}, }
@article {pmid37143057, year = {2023}, author = {Won, K and Kim, H and Gwon, D and Ahn, M and Nam, CS and Jun, SC}, title = {Can vibrotactile stimulation and tDCS help inefficient BCI users?.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {60}, pmid = {37143057}, issn = {1743-0003}, abstract = {Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation's (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter's BCI performance in the vibrotactile stimulation group increased significantly by 9.13% (p < 0.01), and while the tDCS group subjects' performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking values (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers showed no significant stimulation effects across all groups. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users.}, }
@article {pmid37143020, year = {2023}, author = {Batistić, L and Lerga, J and Stanković, I}, title = {Detection of motor imagery based on short-term entropy of time-frequency representations.}, journal = {Biomedical engineering online}, volume = {22}, number = {1}, pages = {41}, pmid = {37143020}, issn = {1475-925X}, abstract = {BACKGROUND: Motor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain-computer interface (BCI). This paper provides a comparison of different time-frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only.<br><br>RESULTS: When using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner-Ville time-frequency representation.<br><br>CONCLUSIONS: Our findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.}, }
@article {pmid37141863, year = {2023}, author = {Ziai, Y and Zargarian, SS and Rinoldi, C and Nakielski, P and Sola, A and Lanzi, M and Truong, YB and Pierini, F}, title = {Conducting polymer-based nanostructured materials for brain-machine interfaces.}, journal = {Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology}, volume = {}, number = {}, pages = {e1895}, doi = {10.1002/wnan.1895}, pmid = {37141863}, issn = {1939-0041}, abstract = {As scientists discovered that raw neurological signals could translate into bioelectric information, brain-machine interfaces (BMI) for experimental and clinical studies have experienced massive growth. Developing suitable materials for bioelectronic devices to be used for real-time recording and data digitalizing has three important necessitates which should be covered. Biocompatibility, electrical conductivity, and having mechanical properties similar to soft brain tissue to decrease mechanical mismatch should be adopted for all materials. In this review, inorganic nanoparticles and intrinsically conducting polymers are discussed to impart electrical conductivity to systems, where soft materials such as hydrogels can offer reliable mechanical properties and a biocompatible substrate. Interpenetrating hydrogel networks offer more mechanical stability and provide a path for incorporating polymers with desired properties into one strong network. Promising fabrication methods, like electrospinning and additive manufacturing, allow scientists to customize designs for each application and reach the maximum potential for the system. In the near future, it is desired to fabricate biohybrid conducting polymer-based interfaces loaded with cells, giving the opportunity for simultaneous stimulation and regeneration. Developing multi-modal BMIs, Using artificial intelligence and machine learning to design advanced materials are among the future goals for this field. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.}, }
@article {pmid37141094, year = {2023}, author = {Zhu, L and Wang, M and Liu, Y and Fu, P and Zhang, W and Zhang, H and Roe, AW and Xi, W}, title = {Single-microvessel occlusion produces lamina-specific microvascular flow vasodynamics and signs of neurodegenerative change.}, journal = {Cell reports}, volume = {42}, number = {5}, pages = {112469}, doi = {10.1016/j.celrep.2023.112469}, pmid = {37141094}, issn = {2211-1247}, abstract = {Recent studies have highlighted the importance of understanding the architecture and function of microvasculature, and dysfunction of these microvessels may underlie neurodegenerative disease. Here, we utilize a high-precision ultrafast laser-induced photothrombosis (PLP) method to occlude single capillaries and then quantitatively study the effects on vasodynamics and surrounding neurons. Analysis of the microvascular architecture and hemodynamics after single-capillary occlusion reveals distinct changes upstream vs. downstream branches, which shows rapid regional flow redistribution and local downstream blood-brain barrier (BBB) leakage. Focal ischemia via capillary occlusions surrounding labeled target neurons induces dramatic and rapid lamina-specific changes in neuronal dendritic architecture. Further, we find that micro-occlusion at two different depths within the same vascular arbor results in distinct effects on flow profiles in layers 2/3 vs layer 4. The current results reveal laminar-scale regulation distinctions in microinfarct response and raise the possibility that relatively greater impacts on microvascular function contribute to cognitive decline in neurodegenerative disease.}, }
@article {pmid37141006, year = {2023}, author = {Wang, Y and Zhao, T and Jiao, Y and Huang, H and Zhang, Y and Fang, A and Wang, X and Zhou, Y and Gu, H and Wu, Q and Chang, J and Li, F and Xu, K}, title = {Silicate Nanoplatelets Promotes Neuronal Differentiation of Neural Stem Cells and Restoration of Spinal Cord Injury.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e2203051}, doi = {10.1002/adhm.202203051}, pmid = {37141006}, issn = {2192-2659}, abstract = {Neural stem cell (NSC) transplantation has been suggested as a promising therapeutic strategy to replace lost neurons after spinal cord injury (SCI). However, the low survival rate and neuronal differentiation efficiency of implanted NSCs within the lesion cavity limit the application. Furthermore, it is difficult for transplanted cells to form connections with host cells. Thus, effective and feasible methods to enhance the efficacy of cell transplantation are needed. In this study, the effect of Laponite nanoplatelets, a type of silicate nanoplatelets, on stem cell therapy was explored. Laponite nanoplatelets could induce the neuronal differentiation of NSCs in vitro within 5 days, and RNA sequencing and protein expression analysis demonstrated that the NF-κB pathway was involved in this process. Moreover, histological results revealed that Laponite nanoplatelets could increase the survival rate of transplanted NSCs and promote NSCs to differentiate into mature neurons. Finally, the formation of connections between transplanted cells and host cells was confirmed by axon tracing. Hence, Laponite nanoplatelets, which drove neuronal differentiation and the maturation of NSCs both in vitro and in vivo, can be considered a convenient and practical biomaterial to promote repair of the injured spinal cord by enhancing the efficacy of NSC transplantation. This article is protected by copyright. All rights reserved.}, }
@article {pmid37140225, year = {2023}, author = {Kosal, M and Putney, J}, title = {Neurotechnology and international security: Predicting commercial and military adoption of brain-computer interfaces (BCIs) in the United States and China.}, journal = {Politics and the life sciences : the journal of the Association for Politics and the Life Sciences}, volume = {42}, number = {1}, pages = {81-103}, doi = {10.1017/pls.2022.2}, pmid = {37140225}, issn = {1471-5457}, abstract = {In the past decade, international actors have launched "brain projects" or "brain initiatives." One of the emerging technologies enabled by these publicly funded programs is brain-computer interfaces (BCIs), which are devices that allow communication between the brain and external devices like a prosthetic arm or a keyboard. BCIs are poised to have significant impacts on public health, society, and national security. This research presents the first analytical framework that attempts to predict the dissemination of neurotechnologies to both the commercial and military sectors in the United States and China. While China started its project later with less funding, we find that it has other advantages that make earlier adoption more likely. We also articulate national security risks implicit in later adoption, including the inability to set international ethical and legal norms for BCI use, especially in wartime operating environments, and data privacy risks for citizens who use technology developed by foreign actors.}, }
@article {pmid37140029, year = {2023}, author = {Zhang, X and Wang, W and Zhang, X and Bai, X and Yuan, Z and Zhang, P and Bai, R and Jiao, B and Zhang, Y and Li, Z and Tang, H and Zhang, Y and Yu, X and Wang, Y and Sui, B}, title = {Normal glymphatic system function in patients with new daily persistent headache using diffusion tensor image analysis along the perivascular space.}, journal = {Headache}, volume = {}, number = {}, pages = {}, doi = {10.1111/head.14514}, pmid = {37140029}, issn = {1526-4610}, abstract = {OBJECTIVES: To investigate the glymphatic function in patients with new daily persistent headache (NDPH) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method.<br><br>BACKGROUND: NDPH, a rare and treatment-refractory primary headache disorder, is poorly understood. There is limited evidence to suggest that headaches are associated with glymphatic dysfunction. Thus far, no studies have evaluated glymphatic function in patients with NDPH.<br><br>METHODS: In this cross-sectional study conducted in the Headache Center of Beijing Tiantan Hospital, patients with NDPH and healthy controls were enrolled. All participants underwent brain magnetic resonance imaging examinations. Clinical characteristics and neuropsychological evaluation were examined in patients with NDPH. ALPS indexes for both hemispheres were measured to determine the glymphatic system function in patients with NDPH and healthy controls.<br><br>RESULTS: In total, 27 patients with NDPH (14 males, 13 females; age [mean&#x2009;&#xb1;&#x2009;standard deviation (SD)]: 36.6&#x2009;&#xb1;&#x2009;20.6) and 33 healthy controls (15 males, 18 females; age [mean&#x2009;&#xb1;&#x2009;SD]: 36.0&#x2009;&#xb1;&#x2009;10.8) were included in the analysis. No significant differences between groups were observed in the left ALPS index (1.583&#x2009;&#xb1;&#x2009;0.182 vs. 1.586&#x2009;&#xb1;&#x2009;0.175, mean difference&#x2009;=&#x2009;0.003, 95% confidence interval [CI] of difference&#x2009;=&#x2009;-0.089 to 0.096, p&#x2009;=&#x2009;0.942), or right ALPS index (1.578&#x2009;&#xb1;&#x2009;0.230 vs. 1.559&#x2009;&#xb1;&#x2009;0.206, mean difference&#x2009;=&#x2009;-0.027, 95% CI of difference&#x2009;=&#x2009;-0.132 to 0.094, p&#x2009;=&#x2009;0.738). Additionally, ALPS indexes were not correlated with clinical characteristics or neuropsychiatric scores.<br><br>CONCLUSION: No glymphatic dysfunction was detected in patients with NDPH by means of the ALPS method. Additional studies with larger samples are needed to confirm these preliminary findings and improve the understanding of glymphatic function in NDPH.}, }
@article {pmid37139769, year = {2023}, author = {Zhang, Z and Zhao, X and Ma, Y and Ding, P and Nan, W and Gong, A and Fu, Y}, title = {[Ethics considerations on brain-computer interface technology].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {40}, number = {2}, pages = {358-364}, doi = {10.7507/1001-5515.202208058}, pmid = {37139769}, issn = {1001-5515}, abstract = {The development and potential application of brain-computer interface (BCI) technology is closely related to the human brain, so that the ethical regulation of BCI has become an important issue attracting the consideration of society. Existing literatures have discussed the ethical norms of BCI technology from the perspectives of non-BCI developers and scientific ethics, while few discussions have been launched from the perspective of BCI developers. Therefore, there is a great need to study and discuss the ethical norms of BCI technology from the perspective of BCI developers. In this paper, we present the user-centered and non-harmful BCI technology ethics, and then discuss and look forward on them. This paper argues that human beings can cope with the ethical issues arising from BCI technology, and as BCI technology develops, its ethical norms will be improved continuously. It is expected that this paper can provide thoughts and references for the formulation of ethical norms related to BCI technology.}, }
@article {pmid37139522, year = {2023}, author = {Li, H and Ji, H and Yu, J and Li, J and Jin, L and Liu, L and Bai, Z and Ye, C}, title = {A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1125230}, pmid = {37139522}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.<br><br>METHODS: This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.<br><br>RESULTS: A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.<br><br>DISCUSSION: This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.}, }
@article {pmid37139055, year = {2023}, author = {Zhu, Y and Wang, C and Li, J and Zeng, L and Zhang, P}, title = {Effect of different modalities of artificial intelligence rehabilitation techniques on patients with upper limb dysfunction after stroke-A network meta-analysis of randomized controlled trials.}, journal = {Frontiers in neurology}, volume = {14}, number = {}, pages = {1125172}, pmid = {37139055}, issn = {1664-2295}, abstract = {BACKGROUND: This study aimed to observe the effects of six different types of AI rehabilitation techniques (RR, IR, RT, RT + VR, VR and BCI) on upper limb shoulder-elbow and wrist motor function, overall upper limb function (grip, grasp, pinch and gross motor) and daily living ability in subjects with stroke. Direct and indirect comparisons were drawn to conclude which AI rehabilitation techniques were most effective in improving the above functions.<br><br>METHODS: From establishment to 5 September 2022, we systematically searched PubMed, EMBASE, the Cochrane Library, Web of Science, CNKI, VIP and Wanfang. Only randomized controlled trials (RCTs) that met the inclusion criteria were included. The risk of bias in studies was evaluated using the Cochrane Collaborative Risk of Bias Assessment Tool. A cumulative ranking analysis by SUCRA was performed to compare the effectiveness of different AI rehabilitation techniques for patients with stroke and upper limb dysfunction.<br><br>RESULTS: We included 101 publications involving 4,702 subjects. According to the results of the SUCRA curves, RT + VR (SUCRA = 84.8%, 74.1%, 99.6%) was most effective in improving FMA-UE-Distal, FMA-UE-Proximal and ARAT function for subjects with upper limb dysfunction and stroke, respectively. IR (SUCRA = 70.5%) ranked highest in improving FMA-UE-Total with upper limb motor function amongst subjects with stroke. The BCI (SUCRA = 73.6%) also had the most significant advantage in improving their MBI daily living ability.<br><br>CONCLUSIONS: The network meta-analysis (NMA) results and SUCRA rankings suggest RT + VR appears to have a greater advantage compared with other interventions in improving upper limb motor function amongst subjects with stroke in FMA-UE-Proximal and FMA-UE-Distal and ARAT. Similarly, IR had shown the most significant advantage over other interventions in improving the FMA-UE-Total upper limb motor function score of subjects with stroke. The BCI also had the most significant advantage in improving their MBI daily living ability. Future studies should consider and report on key patient characteristics, such as stroke severity, degree of upper limb impairment, and treatment intensity/frequency and duration.<br><br>www.crd.york.ac.uk/prospero/#recordDetail, identifier: CRD42022337776.}, }
@article {pmid37138756, year = {2023}, author = {Abdelrahman, Z and Wang, X and Wang, D and Zhang, T and Zhang, Y and Wang, X and Chen, Z}, title = {Identification of novel pathways and immune profiles related to sarcopenia.}, journal = {Frontiers in medicine}, volume = {10}, number = {}, pages = {928285}, pmid = {37138756}, issn = {2296-858X}, abstract = {INTRODUCTION: Sarcopenia is a progressive deterioration of skeletal muscle mass strength and function.<br><br>METHODS: To uncover the underlying cellular and biological mechanisms, we studied the association between sarcopenia's three stages and the patient's ethnicity, identified a gene regulatory network based on motif enrichment in the upregulated gene set of sarcopenia, and compared the immunological landscape among sarcopenia stages.<br><br>RESULTS: We found that sarcopenia (S) was associated with GnRH, neurotrophin, Rap1, Ras, and p53 signaling pathways. Low muscle mass (LMM) patients showed activated pathways of VEGF signaling, B-cell receptor signaling, ErbB signaling, and T-cell receptor signaling. Low muscle mass and physical performance (LMM_LP) patients showed lower enrichment scores in B-cell receptor signaling, apoptosis, HIF-1 signaling, and the adaptive immune response pathways. Five common genes among DEGs and the elastic net regression model, TTC39DP, SLURP1, LCE1C, PTCD2P1, and OR7E109P, were expressed between S patients and healthy controls. SLURP1 and LCE1C showed the highest expression levels among sarcopenic Chinese descent than Caucasians and Afro-Caribbeans. Gene regulatory analysis of top upregulated genes in S patients yielded a top-scoring regulon containing GATA1, GATA2, and GATA3 as master regulators and nine predicted direct target genes. Two genes were associated with locomotion: POSTN and SLURP1. TTC39DP upregulation was associated with a better prognosis and stronger immune profile in S patients. The upregulation of SLURP1 and LCE1C was associated with a worse prognosis and weaker immune profile.<br><br>CONCLUSION: This study provides new insight into sarcopenia's cellular and immunological prospects and evaluates the age and sarcopenia-related modifications of skeletal muscle.}, }
@article {pmid37137713, year = {2023}, author = {Ullah, A and Liu, Y and Wang, Y and Gao, H and Luo, Z and Li, G}, title = {Gender Differences in Taste Sensations Based on Frequency Analysis of Surface Electromyography.}, journal = {Perceptual and motor skills}, volume = {}, number = {}, pages = {315125231169882}, doi = {10.1177/00315125231169882}, pmid = {37137713}, issn = {1558-688X}, abstract = {Males and females respond differently at the muscular level to various tastes and show varied responses when eating different foods. In this study, we used surface electromyography (sEMG) as a novel approach to examine gender differences in taste sensations. We collected sEMG data from 30 participants (15 males, 15 females) over various sessions for six taste states: a no-stimulation physiological state, sweet, sour, salty, bitter, and umami. We applied a Fast Fourier Transformation to the sEMG-filtered data and used a two-sample t-test algorithm to analyze and evaluate the resulting frequency spectrum. Our results showed that the female participants had more sEMG channels with low frequencies and fewer channels with high frequencies than the male participants during all taste states except the bitter taste sensation, meaning that for most sensations, the female participants had better tactile and fewer gustatory responses than the male participants. The female participants responded better to gustatory and tactile perceptions during bitter tasting because they had more channels throughout the frequency distribution. Moreover, the facial muscles of the female participants twitched with low frequencies, while the facial muscles of the male participants twitched with high frequencies for all taste states except the bitter sensation, for which the female facial muscles twitched throughout the range of the frequency distribution. This gender-dependent variation in sEMG frequency distribution provides new evidence of differentiated taste sensations between males and females.}, }
@article {pmid37137387, year = {2023}, author = {Gilbert, F and Ienca, M and Cook, M}, title = {How I became myself after merging with a computer: Does human-machine symbiosis raise human rights issues?.}, journal = {Brain stimulation}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.brs.2023.04.016}, pmid = {37137387}, issn = {1876-4754}, abstract = {Novel usages of brain stimulation combined with artificially intelligent (AI) systems promise to address a large range of diseases. These new conjoined technologies, such as brain-computer interfaces (BCI), are increasingly used in experimental and clinical settings to predict and alleviate symptoms of various neurological and psychiatric disorders. Due to their reliance on AI algorithms for feature extraction and classification, these BCI systems enable a novel, unprecedented, and direct connection between human cognition and artificial information processing. In this paper, we present the results of a study that investigates the phenomenology of human-machine symbiosis during a first-in-human experimental Brain-Computer Interfaces (BCIs) trial designed to predict epileptic seizures. We employed qualitative semi-structured interviews to collect user experience data from participants. We report on a clinical case where a specific embodied phenomenology emerged: namely, after BCI implantation, the patient reported experiences of increased agential capacity and continuity; and after device explantation, the patient reported persistent traumatic harms linked to agential discontinuity. To our knowledge, this is the first reported clinical case of a patient experiencing persistent agential discontinuity due to BCI explantation and potential evidence of an infringement on patient right, where the implanted person was robbed of her de novo agential capacities when the device was removed.}, }
@article {pmid37137282, year = {2023}, author = {Jia, K and Goebel, R and Kourtzi, Z}, title = {Ultra-High Field Imaging of Human Visual Cognition.}, journal = {Annual review of vision science}, volume = {}, number = {}, pages = {}, doi = {10.1146/annurev-vision-111022-123830}, pmid = {37137282}, issn = {2374-4650}, abstract = {Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition. Expected final online publication date for the Annual Review of Vision Science, Volume 9 is September 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.}, }
@article {pmid37133926, year = {2023}, author = {He, Y and Tang, Z and Sun, G and Cai, C and Wang, Y and Yang, G and Bao, Z}, title = {Effectiveness of a Mindfulness Meditation App Based on an Electroencephalography-Based Brain-Computer Interface in Radiofrequency Catheter Ablation for Patients With Atrial Fibrillation: Pilot Randomized Controlled Trial.}, journal = {JMIR mHealth and uHealth}, volume = {11}, number = {}, pages = {e44855}, doi = {10.2196/44855}, pmid = {37133926}, issn = {2291-5222}, abstract = {BACKGROUND: Radiofrequency catheter ablation (RFCA) for patients with atrial fibrillation (AF) can generate considerable physical and psychological discomfort under conscious sedation. App-based mindfulness meditation combined with an electroencephalography (EEG)-based brain-computer interface (BCI) shows promise as effective and accessible adjuncts in medical practice.<br><br>OBJECTIVE: This study aimed to investigate the effectiveness of a BCI-based mindfulness meditation app in improving the experience of patients with AF during RFCA.<br><br>METHODS: This single-center pilot randomized controlled trial involved 84 eligible patients with AF scheduled for RFCA, who were randomized 1:1 to the intervention and control groups. Both groups received a standardized RFCA procedure and a conscious sedative regimen. Patients in the control group were administered conventional care, while those in the intervention group received BCI-based app-delivered mindfulness meditation from a research nurse. The primary outcomes were the changes in the numeric rating scale, State Anxiety Inventory, and Brief Fatigue Inventory scores. Secondary outcomes were the differences in hemodynamic parameters (heart rate, blood pressure, and peripheral oxygen saturation), adverse events, patient-reported pain, and the doses of sedative drugs used in ablation.<br><br>RESULTS: BCI-based app-delivered mindfulness meditation, compared to conventional care, resulted in a significantly lower mean numeric rating scale (mean 4.6, SD 1.7 [app-based mindfulness meditation] vs mean 5.7, SD 2.1 [conventional care]; P=.008), State Anxiety Inventory (mean 36.7, SD 5.5 vs mean 42.3, SD 7.2; P<.001), and Brief Fatigue Inventory (mean 3.4, SD 2.3 vs mean 4.7, SD 2.2; P=.01) scores. No significant differences were observed in hemodynamic parameters or the amounts of parecoxib and dexmedetomidine used in RFCA between the 2 groups. The intervention group exhibited a significant decrease in fentanyl use compared to the control group, with a mean dose of 3.96 (SD 1.37) mcg/kg versus 4.85 (SD 1.25) mcg/kg in the control group (P=.003).The incidence of adverse events was lower in the intervention group (5/40) than in the control group (10/40), though this difference was not significant (P=.15).<br><br>CONCLUSIONS: BCI-based app-delivered mindfulness meditation effectively relieved physical and psychological discomfort and may reduce the doses of sedative medication used in RFCA for patients with AF.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05306015; https://clinicaltrials.gov/ct2/show/NCT05306015.}, }
@article {pmid37132740, year = {2023}, author = {Coelho, HRS and Neves, SC and Menezes, JNS and Antoniolli-Silva, ACMB and Oliveira, RJ}, title = {Cell therapy with adipose tissue-derived human stem cells in the urinary bladder improves detrusor contractility and reduces voiding residue.}, journal = {Brazilian journal of biology = Revista brasleira de biologia}, volume = {83}, number = {}, pages = {e268540}, doi = {10.1590/1519-6984.268540}, pmid = {37132740}, issn = {1678-4375}, mesh = {Humans ; *Urinary Bladder ; *Quality of Life ; Stem Cells ; Cell- and Tissue-Based Therapy ; }, abstract = {Detrusor hypocontractility (DH) is a disease without a gold standard treatment in traditional medicine. Therefore, there is a need to develop innovative therapies. The present report presents the case of a patient with DH who was transplanted with 2 x 106 adipose tissue-derived mesenchymal stem cells twice and achieved significant improvements in their quality of life. The results showed that cell therapy reduced the voiding residue from 1,800 mL to 800 mL, the maximum cystometric capacity from 800 to 550 mL, and bladder compliance from 77 to 36.6 mL/cmH2O. Cell therapy also increased the maximum flow from 3 to 11 mL/s, the detrusor pressure from 08 to 35 cmH2O, the urine volume from 267 to 524 mL and the bladder contractility index (BCI) value from 23 to 90. The International Continence on Incontinence Questionnaire - Short Form score decreased from 17 to 8. Given the above, it is inferred that the transplantation of adipose tissue-derived mesenchymal stem cells is an innovative and efficient therapeutic strategy for DH treatment and improves the quality of life of patients affected by this disease.}, }
@article {pmid37131830, year = {2023}, author = {Deo, DR and Willett, FR and Avansino, DT and Hochberg, LR and Henderson, JM and Shenoy, KV}, title = {Translating deep learning to neuroprosthetic control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.04.21.537581}, pmid = {37131830}, abstract = {Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.}, }
@article {pmid37130514, year = {2023}, author = {Borra, D and Filippini, M and Ursino, M and Fattori, P and Magosso, E}, title = {Motor decoding from the posterior parietal cortex using deep neural networks.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acd1b6}, pmid = {37130514}, issn = {1741-2552}, abstract = {Motor decoding is crucial to translate the neural activity for Brain-Computer Interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless, it is still unclear how different DNNs perform in different motor decoding problems and scenarios, and which network could be a good candidate for invasive BCIs. Approach. Fully-connected, convolutional, and recurrent neural networks (FCNNs, CNNs, RNNs) were designed and applied to decode motor states from neurons recorded from V6A area in the posterior parietal cortex (PPC) of macaques. Three motor tasks were considered, involving reaching and reach-to-grasping (the latter under two illumination conditions). DNNs decoded 9 reaching endpoints in 3-D space or 5 grip types using a sliding window approach within the trial course. To evaluate decoders simulating a broad variety of scenarios, the performance was also analyzed while artificially reducing the number of recorded neurons and trials, and while performing transfer learning from one task to another. Finally, the accuracy time course was used to analyze V6A motor encoding. Main results. DNNs outperformed a classic Naïve Bayes classifier, and CNNs additionally outperformed XGBoost and Support Vector Machine classifiers across the motor decoding problems. CNNs resulted the top-performing DNNs when using less neurons and trials, and task-to-task transfer learning improved performance especially in the low data regime. Lastly, V6A neurons encoded reaching and reach-to-grasping properties even from action planning, with the encoding of grip properties occurring later, closer to movement execution, and appearing weaker in darkness. Significance. Results suggest that CNNs are effective candidates to realize neural decoders for invasive BCIs in humans from PPC recordings also reducing BCI calibration times (transfer learning), and that a CNN-based data-driven analysis may provide insights about the encoding properties and the functional roles of brain regions.}, }
@article {pmid37130248, year = {2023}, author = {Zhang, R and Wang, C and He, S and Zhao, C and Zhang, K and Wang, X and Li, Y}, title = {An Adaptive Brain-Computer Interface to Enhance Motor Recovery After Stroke.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3272372}, pmid = {37130248}, issn = {1558-0210}, abstract = {Brain computer interfaces (BCIs) have been demonstrated to have the potential to enhance motor recovery after stroke. However, some stroke patients with severe paralysis have difficulty achieving the BCI performance required for participating in BCI-based rehabilitative interventions, limiting their clinical benefits. To address this issue, we presented a BCI intervention approach that can adapt to patients' BCI performance and reported that adaptive BCI-based functional electrical stimulation (FES) treatment induced clinically significant, long-term improvements in upper extremity motor function after stroke more effectively than FES treatment without BCI intervention. These improvements were accompanied by a more optimized brain functional reorganization. Further comparative analysis revealed that stroke patients with low BCI performance (LBP) had no significant difference from patients with high BCI performance in rehabilitation efficacy improvement. Our findings suggested that the current intervention may be an effective way for LBP patients to engage in BCI-based rehabilitation treatment and may promote lasting motor recovery, thus contributing to expanding the applicability of BCI-based rehabilitation treatments to pave the way for more effective rehabilitation treatments.}, }
@article {pmid37129900, year = {2023}, author = {Blanco-Díaz, CF and Guerrero-Mendez, CD and Delisle-Rodriguez, D and de Souza, AF and Badue, C and Bastos-Filho, TF}, title = {Lower-limb kinematic reconstruction during pedaling tasks from EEG signals using Unscented Kalman filter.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-11}, doi = {10.1080/10255842.2023.2207705}, pmid = {37129900}, issn = {1476-8259}, abstract = {Kinematic reconstruction of lower-limb movements using electroencephalography (EEG) has been used in several rehabilitation systems. However, the nonlinear relationship between neural activity and limb movement may challenge decoders in real-time Brain-Computer Interface (BCI) applications. This paper proposes a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower-limb kinematics from EEG signals during pedaling. The results demonstrated maximum decoding accuracy using slow cortical potentials in the delta band (0.1-4 Hz) of 0.33 for Pearson's r-value and 8 for the signal-to-noise ratio (SNR). This leaves an open door to the development of closed-loop EEG-based BCI systems for kinematic monitoring during pedaling rehabilitation tasks.}, }
@article {pmid37127759, year = {2023}, author = {Tang, J and LeBel, A and Jain, S and Huth, AG}, title = {Semantic reconstruction of continuous language from non-invasive brain recordings.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {37127759}, issn = {1546-1726}, abstract = {A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain-computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain-computer interfaces.}, }
@article {pmid37125350, year = {2023}, author = {Yu, K and Sun, J and He, B}, title = {Editorial: Brain stimulation and interfacing.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1179166}, doi = {10.3389/fnhum.2023.1179166}, pmid = {37125350}, issn = {1662-5161}, }
@article {pmid37124570, year = {2023}, author = {Donlon, JD and Mee, JF and McAloon, CG}, title = {Prevalence of respiratory disease in Irish preweaned dairy calves using hierarchical Bayesian latent class analysis.}, journal = {Frontiers in veterinary science}, volume = {10}, number = {}, pages = {1149929}, pmid = {37124570}, issn = {2297-1769}, abstract = {INTRODUCTION: Bovine respiratory disease (BRD) has a significant impact on the health and welfare of dairy calves. It can result in increased antimicrobial usage, decreased growth rate and reduced future productivity. There is no gold standard antemortem diagnostic test for BRD in calves and no estimates of the prevalence of respiratory disease in seasonal calving dairy herds.<br><br>METHODS: To estimate BRD prevalence in seasonal calving dairy herds in Ireland, 40 dairy farms were recruited and each farm was visited once during one of two calving seasons (spring 2020 &amp; spring 2021). At that visit the prevalence of BRD in 20 calves between 4 and 6 weeks of age was determined using thoracic ultrasound score (&#x2265;3) and the Wisconsin respiratory scoring system (&#x2265;5). Hierarchical Bayesian latent class analysis was used to estimate the calf-level true prevalence of BRD, and the within-herd prevalence distribution, accounting for the imperfect nature of both diagnostic tests.<br><br>RESULTS: In total, 787 calves were examined, of which 58 (7.4%) had BRD as defined by a Wisconsin respiratory score &#x2265;5 only, 37 (4.7%) had BRD as defined by a thoracic ultrasound score of &#x2265;3 only and 14 (1.8%) calves had BRD based on both thoracic ultrasound and clinical scoring. The primary model assumed both tests were independent and used informed priors for test characteristics. Using this model the true prevalence of BRD was estimated as 4%, 95% Bayesian credible interval (BCI) (1%, 8%). This prevalence estimate is lower or similar to those found in other dairy production systems. Median within herd prevalence varied from 0 to 22%. The prevalence estimate was not sensitive to whether the model was constructed with the tests considered conditionally dependent or independent. When the case definition for thoracic ultrasound was changed to a score &#x2265;2, the prevalence estimate increased to 15% (95% BCI: 6%, 27%).<br><br>DISCUSSION: The prevalence of calf respiratory disease, however defined, was low, but highly variable, in these seasonal calving dairy herds.}, }
@article {pmid37121218, year = {2023}, author = {Li, S and Huang, S and Hu, S and Lai, J}, title = {Psychological consequences among veterans during the COVID-19 pandemic: A scoping review.}, journal = {Psychiatry research}, volume = {324}, number = {}, pages = {115229}, pmid = {37121218}, issn = {1872-7123}, abstract = {Although there is an increasing number of studies reporting the psychological impact of COVID-19 on the general population and healthcare workers, relatively less attention has been paid to the veterans. This study aimed to review the existing literature regarding the psychological consequences of COVID-19 on veterans. A systematic search was conducted on PubMed, Embase, and the Cochrane Library from inception to December 3, 2022. A total of twenty-three studies were included with moderate-quality of evidence. Veterans experienced more mental health problems than civilians. The prevalence rates of alcohol use, anxiety, depression, post-traumatic stress disorder, stress, loneliness, and suicide ideation significantly increased during the pandemic, ranging from 9.6% to 47.4%, 9.4% to 53.5%, 8.6% to 55.1%, 4.1% to 58.0%, 4.3% to 39.4%, 15.9% to 28.4%, and 7.8% to 22.0%, respectively. The main risk factors of negative consequences included pandemic-related stress, poor family relationships, lack of social support, financial problems, and preexisting mental disorders. In contrast, higher household income and greater community interaction and support appeared to be resilience factors. In conclusion, the COVID-19 pandemic has increased adverse mental health consequences among veterans. Tackling mental health issues due to the COVID-19 pandemic among veterans should be a priority.}, }
@article {pmid37118880, year = {2023}, author = {Liu, H and Tang, M and Yu, H and Liu, T and Cui, Q and Gu, L and Wu, Z and Sheng, N and Yang, XL and Zeng, L and Bai, G}, title = {CMT2D neuropathy is influenced by vitamin D-mediated environmental pathway.}, journal = {Journal of molecular cell biology}, volume = {}, number = {}, pages = {}, doi = {10.1093/jmcb/mjad029}, pmid = {37118880}, issn = {1759-4685}, }
@article {pmid37116888, year = {2023}, author = {Bergeron, D and Iorio-Morin, C and Bonizzato, M and Lajoie, G and Orr Gaucher, N and Racine, &#xc9; and Weil, AG}, title = {Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations.}, journal = {Journal of child neurology}, volume = {}, number = {}, pages = {8830738231167736}, doi = {10.1177/08830738231167736}, pmid = {37116888}, issn = {1708-8283}, abstract = {Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.}, }
@article {pmid37118342, year = {2021}, author = {Chen, S and Chen, L and Qi, Y and Xu, J and Ge, Q and Fan, Y and Chen, D and Zhang, Y and Wang, L and Hou, T and Yang, X and Xi, Y and Si, J and Kang, L and Wang, L}, title = {Bifidobacterium adolescentis regulates catalase activity and host metabolism and improves healthspan and lifespan in multiple species.}, journal = {Nature aging}, volume = {1}, number = {11}, pages = {991-1001}, pmid = {37118342}, issn = {2662-8465}, mesh = {Animals ; Mice ; *Bifidobacterium adolescentis ; Longevity ; Caenorhabditis elegans/genetics ; Catalase ; Drosophila melanogaster ; Fibroblasts ; }, abstract = {To identify candidate bacteria associated with aging, we performed fecal microbiota sequencing in young, middle-aged and older adults, and found lower Bifidobacterium adolescentis abundance in older individuals aged ≥60 years. Dietary supplementation of B. adolescentis improved osteoporosis and neurodegeneration in a mouse model of premature aging (Terc[-/-]) and increased healthspan and lifespan in Drosophila melanogaster and Caenorhabditis elegans. B. adolescentis supplementation increased the activity of the catalase (CAT) enzyme in skeletal muscle and brain tissue from Terc[-/-] mice, and suppressed cellular senescence in mouse embryonic fibroblasts. Transgenic deletion of catalase (ctl-2) in C. elegans abolished the effects of B. adolescentis on the lifespan and healthspan. B. adolescentis feeding also led to changes in oxidative stress-associated metabolites in Terc[-/-] mouse feces. These results suggest a role for B. adolescentis in improving the healthspan and lifespan through the regulation of CAT activity and host metabolism.}, }
@article {pmid37113774, year = {2023}, author = {Aghili, SN and Kilani, S and Khushaba, RN and Rouhani, E}, title = {A spatial-temporal linear feature learning algorithm for P300-based brain-computer interfaces.}, journal = {Heliyon}, volume = {9}, number = {4}, pages = {e15380}, pmid = {37113774}, issn = {2405-8440}, abstract = {Speller brain-computer interface (BCI) systems can help neuromuscular disorders patients write their thoughts by using the electroencephalogram (EEG) signals by just focusing on the speller tasks. For practical speller-based BCI systems, the P300 event-related brain potential is measured by using the EEG signal. In this paper, we design a robust machine-learning algorithm for P300 target detection. The novel spatial-temporal linear feature learning (STLFL) algorithm is proposed to extract high-level P300 features. The STLFL method is a modified linear discriminant analysis technique focusing on the spatial-temporal aspects of information extraction. A new P300 detection structure is then proposed based on the combination of the novel STLFL feature extraction and discriminative restricted Boltzmann machine (DRBM) for the classification approach (STLFL + DRBM). The effectiveness of the proposed technique is evaluated using two state-of-the-art P300 BCI datasets. Across the two available databases, we show that in terms of average target recognition accuracy and standard deviation values, the proposed STLFL + DRBM method outperforms traditional methods by 33.5, 78.5, 93.5, and 98.5% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition III datasets II and by 71.3, 100, 100, and 100% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition II datasets II and by 67.5 ± 4, 84.2 ± 2.5, 93.5 ± 1, 96.3 ± 1, and 98.4 ± 0.5% for rapid serial visual presentation (RSVP) based dataset in repetitions 1-5. The method has some advantages over the existing variants including its efficiency, robustness with a small number of training samples, and a high ability to create discriminative features between classes.}, }
@article {pmid37112504, year = {2023}, author = {Mwata-Velu, T and Niyonsaba-Sebigunda, E and Avina-Cervantes, JG and Ruiz-Pinales, J and Velu-A-Gulenga, N and Alonso-Ramírez, AA}, title = {Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {8}, pages = {}, pmid = {37112504}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Imagery, Psychotherapy ; Software ; }, abstract = {Nowadays, Brain-Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT's public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems' requirements, dealing with short processing times and reliable classification accuracy.}, }
@article {pmid37112230, year = {2023}, author = {Al-Qazzaz, NK and Aldoori, AA and Ali, SHBM and Ahmad, SA and Mohammed, AK and Mohyee, MI}, title = {EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients' Rehabilitation.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {8}, pages = {}, pmid = {37112230}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Stroke ; Imagery, Psychotherapy ; Electroencephalography/methods ; Algorithms ; }, abstract = {The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain-computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals' performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (LE), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that LE with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke.}, }
@article {pmid37106582, year = {2023}, author = {Mesin, L and Cipriani, GE and Amanzio, M}, title = {Electroencephalography-Based Brain-Machine Interfaces in Older Adults: A Literature Review.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {4}, pages = {}, pmid = {37106582}, issn = {2306-5354}, abstract = {The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain-machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users' needs are considered.}, }
@article {pmid37106143, year = {2023}, author = {Koitschev, A and Neudert, M and Lenarz, T}, title = {[Transcutaneous bone conduction implant with self-drilling screws : A new method for fixation of an active transcutaneous bone conduction implant. German version].}, journal = {HNO}, volume = {}, number = {}, pages = {}, pmid = {37106143}, issn = {1433-0458}, abstract = {BACKGROUND: The active transcutaneous bone conduction implant (tBCI; BONEBRIDGE™ BCI 601; MED-EL, Innsbruck, Austria) is fixed to the skull with two self-tapping screws in predrilled screw channels. The aim of this prospective study was to evaluate the safety and effectiveness of fixation with self-drilling screws instead of the self-tapping screws, in order to simplify the surgical procedure.<br><br>MATERIALS AND METHODS: Nine patients (mean age 37&#x202f;&#xb1;&#x2009;16&#xa0;years, range 14-57&#xa0;years) were examined pre- and 12&#xa0;months postoperatively for word recognition scores (WRS) at 65&#x202f;dB SPL, sound-field (SF) thresholds, bone conduction thresholds (BC), health-related quality of life (Assessment of Quality of Life, AQOL-8D questionnaire), and adverse events (AE).<br><br>RESULTS: Due to avoidance of one surgical step, the surgical technique was simplified. Mean WRS in SF was 11.1&#x202f;&#xb1;&#x2009;22.2% (range 0-55%) pre- and 77.2&#x202f;&#xb1;&#x2009;19.9% (range 30-95%) postoperatively; mean SF threshold (pure tone audiometry, PTA4) improved from 61.2&#x202f;&#xb1;&#x2009;14.3&#x202f;dB HL (range 37.0-75.3&#x202f;dB HL) to 31.9&#x202f;&#xb1;&#x2009;7.2&#x202f;dB HL (range 22.8-45.0&#x202f;dB HL); mean BC thresholds were constant at 16.7&#x202f;&#xb1;&#x2009;6.8&#x202f;dB HL (range 6.3-27.5&#x202f;dB HL) pre- and 14.2&#x202f;&#xb1;&#x2009;6.2&#x202f;dB HL (range 5.8-23.8&#x202f;dB HL) postoperatively. AQOL-8D mean utility score increased from 0.65&#x202f;&#xb1;&#x2009;0.18 preoperatively to 0.82&#x202f;&#xb1;&#x2009;0.17 postoperatively. No device-related adverse events occurred.<br><br>CONCLUSION: Implant fixation by means of self-drilling screws was safe and effective in all nine patients. There was significant audiological benefit 12&#xa0;months after implantation.}, }
@article {pmid37105806, year = {2023}, author = {Neumann, WJ and Horn, A and Kühn, AA}, title = {Insights and opportunities for deep brain stimulation as a brain circuit intervention.}, journal = {Trends in neurosciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tins.2023.03.009}, pmid = {37105806}, issn = {1878-108X}, abstract = {Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.}, }
@article {pmid37101843, year = {2022}, author = {Portes, JP and Schmid, C and Murray, JM}, title = {Distinguishing Learning Rules with Brain Machine Interfaces.}, journal = {Advances in neural information processing systems}, volume = {35}, number = {}, pages = {25937-25950}, pmid = {37101843}, issn = {1049-5258}, support = {R00 NS114194/NS/NINDS NIH HHS/United States ; }, abstract = {Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. We derive a metric to distinguish between learning rules by observing changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for precise knowledge of this mapping, we model a cursor-control BMI task using recurrent neural networks, showing that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.}, }
@article {pmid37097795, year = {2023}, author = {Chung, M and Kim, T and Jeong, E and Chung, CK and Kim, JS and Kwon, OS and Kim, SP}, title = {Decoding Imagined Musical Pitch from Human Scalp Electroencephalograms.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3270175}, pmid = {37097795}, issn = {1558-0210}, abstract = {Brain-computer interfaces (BCIs) can restore impaired cognitive functions in people with neurological disorders such as stroke. Musical ability is a cognitive function that is correlated with non-musical cognitive functions, and restoring it can enhance other cognitive functions. Pitch sense is the most relevant function to musical ability according to previous studies of amusia, and thus decoding pitch information is crucial for BCIs to be able to restore musical ability. This study evaluated the feasibility of decoding pitch imagery information directly from human electroencephalography (EEG). Twenty participants performed a random imagery task with seven musical pitches (C4-B4). We used two approaches to explore EEG features of pitch imagery: multiband spectral power at individual channels (IC) and differences between bilaterally symmetric channels (DC). The selected spectral power features revealed remarkable contrasts between left and right hemispheres, low- (<13 Hz) and high-frequency (> 13 Hz) bands, and frontal and parietal areas. We classified two EEG feature sets, IC and DC, into seven pitch classes using five types of classifiers. The best classification performance for seven pitches was obtained using IC and multiclass Support Vector Machine with an average accuracy of 35.68±7.47% (max. 50%) and an information transfer rate (ITR) of 0.37±0.22 bits/sec. When grouping the pitches to vary the number of classes (K = 2-6), the ITR was similar across K and feature sets, suggesting the efficiency of DC. This study demonstrates for the first time the feasibility of decoding imagined musical pitch directly from human EEG.}, }
@article {pmid37095297, year = {2023}, author = {Wu, Z and Tang, X and Wu, J and Huang, J and Shen, J and Hong, H}, title = {Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {37095297}, issn = {1741-0444}, abstract = {Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the application of deep-learning techniques in independent portable brain-computer-interface (BCI) devices has not been extensively explored. In this study, we proposed a high-accuracy MI EEG decoder by incorporating spatial-attention mechanism into convolution neural network (CNN), and deployed it on fully integrated single-chip microcontroller unit (MCU). After the CNN model was trained on workstation computer using GigaDB MI datasets (52 subjects), its parameters were then extracted and converted to build deep-learning architecture interpreter on MCU. For comparison, EEG-Inception model was also trained using the same dataset, and was deployed on MCU. The results indicate that our deep-learning model can independently decode imaginary left-/right-hand motions. The mean accuracy of the proposed compact CNN reaches 96.75 ± 2.41% (8 channels: Frontocentral3 (FC3), FC4, Central1 (C1), C2, Central-Parietal1 (CP1), CP2, C3, and C4), versus 76.96 ± 19.08% of EEG-Inception (6 channels: FC3, FC4, C1, C2, CP1, and CP2). To the best of our knowledge, this is the first portable deep-learning decoder for MI EEG signals. The findings demonstrate high-accuracy deep-learning decoding of MI EEG in a portable mode, which has great implications for hand-disabled patients. Our portable system can be used for developing artificial-intelligent wearable BCI devices, as it is less computationally expensive and convenient for real-life application.}, }
@article {pmid37094717, year = {2023}, author = {Jiang, Z and Liu, Y and Li, W and Dai, Y and Zou, L}, title = {Integration of Simultaneous fMRI and EEG source localization in emotional decision problems.}, journal = {Behavioural brain research}, volume = {}, number = {}, pages = {114445}, doi = {10.1016/j.bbr.2023.114445}, pmid = {37094717}, issn = {1872-7549}, abstract = {Simultaneous EEG-fMRI has been a powerful technique to understand the mechanism of the brain in recent years. In this paper, we develop an integrating method by integrating the EEG data into the fMRI data based on the parametric empirical Bayesian (PEB) model to improve the accuracy of the brain source location. The gambling task, a classic paradigm, is used for the emotional decision-making study in this paper. The proposed method was conducted on 21 participants, including 16 men and 5 women. Contrary to the previous method that only localizes the area widely distributed across the ventral striatum and orbitofrontal cortex, the proposed method localizes accurately at the orbital frontal cortex during the process of the brain's emotional decision-making. The activated brain regions extracted by source localization were mainly located in the prefrontal and orbitofrontal lobes; the activation of the temporal pole regions unrelated to reward processing disappeared, and the activation of the somatosensory cortex and motor cortex was significantly reduced. The log evidence shows that the integration of simultaneous fMRI and EEG method based on synchronized data evidence is 22420, the largest value among the three methods. The integration method always takes on a larger value of log evidence and describes a better performance in analysis associated with source localization. DATA AVAILABILITY: The data used in the current study are available from the corresponding authouponon reasonable request.}, }
@article {pmid37093731, year = {2023}, author = {Li, Y and Chen, B and Shi, Y and Yoshimura, N and Koike, Y}, title = {Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3246599}, pmid = {37093731}, issn = {1558-2531}, abstract = {OBJECTIVE: Recent studies have used sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's mental states and intentions, selecting the relevant features automatically in the model training process. However, existing sparse classification models will likely be prone to the performance degradation which is caused by the noise inherent in the brain recordings. To address this issue, we aim to propose a new robust and sparse classification algorithm in this study.<br><br>METHODS: To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm. To demonstrate the superior brain activity decoding performance of the proposed algorithm, we evaluate it on a synthetic dataset, an electroencephalogram (EEG) dataset, and a functional magnetic resonance imaging (fMRI) dataset.<br><br>RESULTS: The extensive experimental results confirm that not only the proposed method can achieve higher classification accuracy in a noisy and high-dimensional classification task, but also it would select those more informative features for the decoding tasks.<br><br>CONCLUSION: Integrating the correntropy learning approach with the automatic relevance determination technique will significantly improve the robustness with respect to the noise, leading to more adequate robust sparse brain decoding algorithm.<br><br>SIGNIFICANCE: It provides a more powerful approach in the real-world brain activity decoding and the brain-computer interfaces.}, }
@article {pmid37092505, year = {2023}, author = {Mayorova, L and Kushnir, A and Sorokina, V and Pradhan, P and Radutnaya, M and Zhdanov, V and Petrova, M and Grechko, A}, title = {Rapid Effects of BCI-Based Attention Training on Functional Brain Connectivity in Poststroke Patients: A Pilot Resting-State fMRI Study.}, journal = {Neurology international}, volume = {15}, number = {2}, pages = {549-559}, pmid = {37092505}, issn = {2035-8385}, abstract = {The prevalence of stroke-induced cognitive impairment is high. Effective approaches to the treatment of these cognitive impairments after stroke remain a serious and perhaps underestimated challenge. A BCI-based task-focused training that results in repetitive recruitment of the normal motor or cognitive circuits may strengthen stroke-affected neuronal connectivity, leading to functional improvements. In the present controlled study, we attempted to evaluate the modulation of neuronal circuits under the influence of 10 days of training in a P3-based BCI speller in subacute ischemic stroke patients.}, }
@article {pmid37090803, year = {2023}, author = {Urdaneta, ME and Kunigk, NG and Peñaloza-Aponte, JD and Currlin, S and Malone, IG and Fried, SI and Otto, KJ}, title = {Layer-dependent stability of intracortical recordings and neuronal cell loss.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1096097}, pmid = {37090803}, issn = {1662-4548}, abstract = {Intracortical recordings can be used to voluntarily control external devices via brain-machine interfaces (BMI). Multiple factors, including the foreign body response (FBR), limit the stability of these neural signals over time. Current clinically approved devices consist of multi-electrode arrays with a single electrode site at the tip of each shank, confining the recording interface to a single layer of the cortex. Advancements in manufacturing technology have led to the development of high-density electrodes that can record from multiple layers. However, the long-term stability of neural recordings and the extent of neuronal cell loss around the electrode across different cortical depths have yet to be explored. To answer these questions, we recorded neural signals from rats chronically implanted with a silicon-substrate microelectrode array spanning the layers of the cortex. Our results show the long-term stability of intracortical recordings varies across cortical depth, with electrode sites around L4-L5 having the highest stability. Using machine learning guided segmentation, our novel histological technique, DeepHisto, revealed that the extent of neuronal cell loss varies across cortical layers, with L2/3 and L4 electrodes having the largest area of neuronal cell loss. These findings suggest that interfacing depth plays a major role in the FBR and long-term performance of intracortical neuroprostheses.}, }
@article {pmid37089477, year = {2023}, author = {Zhang, T and Chen, WT and He, Q and Li, Y and Peng, H and Xie, J and Hu, H and Qin, C}, title = {Coping strategies following the diagnosis of a fetal anomaly: A scoping review.}, journal = {Frontiers in public health}, volume = {11}, number = {}, pages = {1055562}, pmid = {37089477}, issn = {2296-2565}, mesh = {Female ; Pregnancy ; Humans ; *Adaptation, Psychological ; *Pregnant Women ; Anxiety ; Qualitative Research ; }, abstract = {INTRODUCTION: Many women experience severe emotional distress (such as grief, depression, and anxiety) following a diagnosis of fetal anomaly. The ability to cope with stressful events and regulate emotions across diverse situations may play a primary role in psychological wellbeing. This study aims to present coping strategies after disclosing a fetal anomaly to pregnant women.<br><br>METHODS: This is a scoping review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews (PRISMA-ScR). Electronic databases, including Web of Science (WOS, BCI, KJD, MEDLINE, RSCI, SCIELO), CINAHL, and EBSCO PsycARTICLES, were used to search for primary studies from the inception of each database to 2021. The keywords were determined by existing literature and included: "fetal anomaly," "fetal abnormality," "fetal anomaly," "fetal abnormality" AND "cope," "coping," "deal," "manage," "adapt[*]," "emotion[*] regulate[*]," with the use of Boolean operators AND/OR. A total of 16 articles were reviewed, followed by advancing scoping review methodology of Arksey and O'Malley's framework.<br><br>RESULTS: In this review, we identified 52 coping strategies using five questionnaires in seven quantitative studies and one mixed-method study. The relationship between coping strategies and mental distress was explored. However, the results were inconsistent and incomparable. We synthesized four coping categories from qualitative studies and presented them in an intersection.<br><br>CONCLUSION: This scoping review identified the coping strategies of women with a diagnosis of a fetal anomaly during pregnancy. The relationship between coping strategies and mental distress was uncertain and needs more exploration. We considered an appropriate measurement should be necessary for the research of coping in women diagnosed with fetal anomaly pregnancy.}, }
@article {pmid37084719, year = {2023}, author = {Nason-Tomaszewski, SR and Mender, MJ and Kennedy, E and Lambrecht, JM and Kilgore, KL and Chiravuri, S and Ganesh Kumar, N and Kung, TA and Willsey, MS and Chestek, CA and Patil, PG}, title = {Restoring continuous finger function with temporarily paralyzed nonhuman primates using brain-machine interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/accf36}, pmid = {37084719}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user's own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand.<br><br>APPROACH: We delivered a nerve block to the median, radial, and ulnar nerves just proximal to the elbow to simulate finger paralysis, then used a closed-loop BMI to predict finger movements the monkey was attempting to make in two tasks. The BCFES task was one-dimensional in which all fingers moved together, and we used the BMI's predictions to control FES of the monkey's finger muscles. The virtual two-finger task was two-dimensional in which the index finger moved simultaneously and independently from the middle, ring, and small fingers, and we used the BMI's predictions to control movements of virtual fingers, with no FES.<br><br>MAIN RESULTS: In the BCFES task, the monkey improved his success rate to 83% (1.5s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5s median acquisition time, equal to the trial timeout) when attempting to use his temporarily paralyzed hand. In one monkey performing the virtual two-finger task with no FES, we found BMI performance (task success rate and completion time) could be completely recovered following temporary paralysis by executing recalibrated feedback-intention training one time.<br><br>SIGNIFICANCE: These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting factor in a BCFES neuroprosthesis.}, }
@article {pmid37083516, year = {2023}, author = {Fan, Z and Xi, X and Gao, Y and Wang, T and Fang, F and Houston, M and Zhang, Y and Li, L and Lu, Z}, title = {Joint Filter-Band-Combination and Multi-view CNN for Electroencephalogram Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3269055}, pmid = {37083516}, issn = {1558-0210}, abstract = {Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on parameter design to extract features, whereas deep learning algorithms represented by convolution neural network (CNN) can automatically extract features, which is more suitable for BCI applications. However, when EEG data is taken as input in raw time series, traditional 1D-CNNs are unable to acquire both frequency domain and channel association information. To solve this problem, this study proposes a novel algorithm by inserting two modules into CNN. One is the Filter Band Combination (FBC) Module, which preserves as many frequency domain features as possible while maintaining the time domain characteristics of EEG. Another module is Multi-View structure that can extract features from the output of FBC module. To prevent over fitting, we used a cosine annealing algorithm with restart strategy to update the learning rate. The proposed algorithm was validated on the BCI competition dataset and the experiment dataset, using accuracy, standard deviation, and kappa coefficient. Compared with traditional decoding algorithms, our proposed algorithm achieved an improvement of the maximum average correct rate of 6.6% on the motion imagery 4-classes recognition mission and 11.3% on the 2-classes classification task.}, }
@article {pmid37083413, year = {2023}, author = {Lim, H and Jeong, CH and Kang, YJ and Ku, J}, title = {Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients.}, journal = {Cyberpsychology, behavior and social networking}, volume = {}, number = {}, pages = {}, doi = {10.1089/cyber.2022.0176}, pmid = {37083413}, issn = {2152-2723}, abstract = {Brain-computer interface (BCI) is a promising technique that enables patients' interaction with computers or machines by analyzing specific brain signal patterns and provides patients with brain state-dependent feedback to assist in their rehabilitation. Action observation (AO) and peripheral electrical stimulation (PES) are conventional methods used to enhance rehabilitation outcomes by promoting neural plasticity. In this study, we assessed the effects of attentional state-dependent feedback in the combined application of BCI-AO with PES on sensorimotor cortical activation in patients after stroke. Our approach involved showing the participants a video with repetitive grasping actions under four different tasks. A mu band suppression (8-13 Hz) corresponding to each task was computed. A topographical representation showed that mu suppression of the dominant (healthy) and affected hemispheres (stroke) gradually became prominent during the tasks. There were significant differences in mu suppression in the affected motor and frontal cortices of the stroke patients. The involvement of both frontal and motor cortices became prominent in the BCI-AO+triggered PES task, in which feedback was given to the patients according to their attentive watching. Our findings suggest that synchronous stimulation according to patient attention is important for neurorehabilitation of stroke patients, which can be achieved with the combination of BCI-AO feedback with PES. BCI-AO feedback combined with PES could be effective in facilitating sensorimotor cortical activation in the affected hemispheres of stroke patients.}, }
@article {pmid37082149, year = {2023}, author = {Müller-Putz, GR and Collinger, JL and Kobler, RJ}, title = {Editorial: Towards dependable brain computer/machine interfaces for movement control.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1186423}, doi = {10.3389/fnhum.2023.1186423}, pmid = {37082149}, issn = {1662-5161}, }
@article {pmid37081753, year = {2023}, author = {Nath, RK and Somasundaram, C}, title = {Double Fascicular Nerve Transfer Restored Nearly Normal Functional Movements in a Completely Paralyzed Upper Extremity Resulting from an ACDF Surgery: A Case Report and Review of Recent Literature.}, journal = {The American journal of case reports}, volume = {24}, number = {}, pages = {e938650}, doi = {10.12659/AJCR.938650}, pmid = {37081753}, issn = {1941-5923}, mesh = {Female ; Humans ; Middle Aged ; *Nerve Transfer/methods ; *Brachial Plexus/injuries ; Muscle, Skeletal ; Paralysis/surgery ; Upper Extremity/surgery ; }, abstract = {BACKGROUND Cervical spine deformities can occur because of genetic, congenital, inflammatory, degenerative, or iatrogenic causes. CASE REPORT We report a 45-year-old woman who presented to our clinic with complete paralysis of the left upper extremity 5 months after C4-C6 discectomy and fusion surgery. The electrodiagnostic and EMG reports 3 months after her previous surgery revealed left C5-C7 polyradiculopathy involving the upper trunk, lateral and posterior cords, and atrophy of the left deltoids, triceps, and biceps muscles. She underwent the following nerve transfer procedures with the senior author (RKN): The median nerve fascicles were transferred to the biceps and brachialis branches of the musculocutaneous nerve. Radial nerve triceps branches were transferred to the deltoid and teres minor branches of the axillary nerve. The patient could fully abduct her left shoulder to 170°, and the LUE functions were restored to nearly normal 17 months after the surgery. CONCLUSIONS Neurolysis combined with nerve transfer might be the most effective treatment for cervical spinal root injuries. Advances in peripheral nerve rewiring, transcranial magnetic stimulation, brain-computer interface robotic technologies, and emerging rehabilitation will undoubtedly increase the possibility of reviving the extremities in patients with central pathology by restoring the descending motor signals through the residual neural network connections.}, }
@article {pmid37081142, year = {2023}, author = {Shen, K and Chen, O and Edmunds, JL and Piech, DK and Maharbiz, MM}, title = {Translational opportunities and challenges of invasive electrodes for neural interfaces.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {37081142}, issn = {2157-846X}, abstract = {Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.}, }
@article {pmid37080420, year = {2023}, author = {Shang, Q and Chen, J and Fu, H and Wang, C and Pei, G and Jin, J}, title = {"Guess You Like It" - How personalized recommendation timing and product type influence consumers' acceptance: An ERP study.}, journal = {Neuroscience letters}, volume = {}, number = {}, pages = {137261}, doi = {10.1016/j.neulet.2023.137261}, pmid = {37080420}, issn = {1872-7972}, abstract = {Personalized recommendation has been increasingly used in online shopping environment, and improving the effectiveness of personalized recommendation is an important issue. On the basis of two-stage decision theory and preference inconsistency theory, our study adopted the neuroscientific methodology of event-related potential to investigate the decision-making process and psychological mechanism of consumers for personalized recommendation under different recommendation timings (browsing and decision stages) and recommended product types (similar and related). Behavioral results showed that consumers' acceptance of similar product recommendations was higher than that of related product recommendations during the browsing stage, whereas no difference was observed in consumers' acceptance of the two product types during the decision stage. More importantly, neurophysiology results provided underlying psychological mechanism for exploring consumers' decision-making process for personalized recommendations. Consumers' psychological mechanism of the personalized recommendations might be divided into two processes, the early automatic cognitive process indicated by the N2 component, and the late advanced cognitive process indicated by the P3 component. We suggested that N2 reflects the perceptual mismatch between the recommended products and the target products, and P3 reflects the attention capture during categorization evaluation of the recommended product and the target product. These findings have important theoretical and practical significance for the deeper understanding of consumers' decision-making process and psychological mechanism in personalized recommendation, as well as improving the effectiveness of personalized recommendation.}, }
@article {pmid37080210, year = {2023}, author = {Zhang, Z and Constandinou, T}, title = {Firing-rate-modulated spike detection and neural decoding co-design.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/accece}, pmid = {37080210}, issn = {1741-2552}, abstract = {OBJECTIVE: Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding. Approach: We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance over months.<br><br>MAIN RESULTS: We demonstrate a multiplication-free fixed-point spike detection algorithm with 97% detection accuracy and the lowest complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%.&#xD;Significance: Our findings regarding the relationship between spike detection and decoding performance can provide guidance on setting the threshold for spike detection rather than relying on training or trial-and-error. The trade-off between data bandwidth and decoding performance can be effectively managed using appropriate spike detection settings. We demonstrate improved decoding performance by maintaining statistical invariance of input features. We believe this approach can motivate further research focused on improving decoding performance through the manipulation of data itself (based on a hypothesis) rather than using more complex decoding models.}, }
@article {pmid37080005, year = {2023}, author = {She, Q and Shi, X and Fang, F and Ma, Y and Zhang, Y}, title = {Cross-subject EEG emotion recognition using multi-source domain manifold feature selection.}, journal = {Computers in biology and medicine}, volume = {159}, number = {}, pages = {106860}, doi = {10.1016/j.compbiomed.2023.106860}, pmid = {37080005}, issn = {1879-0534}, abstract = {Recent researches on emotion recognition suggests that domain adaptation, a form of transfer learning, has the capability to solve the cross-subject problem in Affective brain-computer interface (aBCI) field. However, traditional domain adaptation methods perform single to single domain transfer or simply merge different source domains into a larger domain to realize the transfer of knowledge, resulting in negative transfer. In this study, a multi-source transfer learning framework was proposed to promote the performance of multi-source electroencephalogram (EEG) emotion recognition. The method first used the data distribution similarity ranking (DDSA) method to select the appropriate source domain for each target domain off-line, and reduced data drift between domains through manifold feature mapping on Grassmann manifold. Meanwhile, the minimum redundancy maximum correlation algorithm (mRMR) was employed to select more representative manifold features and minimized the conditional distribution and marginal distribution of the manifold features, and then learned the domain-invariant classifier by summarizing structural risk minimization (SRM). Finally, the weighted fusion criterion was applied to further improve recognition performance. We compared our method with several state-of-the-art domain adaptation techniques using the SEED and DEAP dataset. Results showed that, compared with the conventional MEDA algorithm, the recognition accuracy of our proposed algorithm on SEED and DEAP dataset were improved by 6.74% and 5.34%, respectively. Besides, compared with TCA, JDA, and other state-of-the-art algorithms, the performance of our proposed method was also improved with the best average accuracy of 86.59% on SEED and 64.40% on DEAP. Our results demonstrated that the proposed multi-source transfer learning framework is more effective and feasible than other state-of-the-art methods in recognizing different emotions by solving the cross-subject problem.}, }
@article {pmid37079422, year = {2023}, author = {Zhou, J and Duan, Y and Zou, Y and Chang, YC and Wang, YK and Lin, CT}, title = {Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3268751}, pmid = {37079422}, issn = {1558-0210}, abstract = {Identifying meaningful brain activities is critical in brain-computer interface (BCI) applications. Recently, an increasing number of neural network approaches have been proposed to recognize EEG signals. However, these approaches depend heavily on using complex network structures to improve the performance of EEG recognition and suffer from the deficit of training data. Inspired by the waveform characteristics and processing methods shared between EEG and speech signals, we propose Speech2EEG, a novel EEG recognition method that leverages pretrained speech features to improve the accuracy of EEG recognition. Specifically, a pretrained speech processing model is adapted to the EEG domain to extract multichannel temporal embeddings. Then, several aggregation methods, including the weighted average, channelwise aggregation, and channel-and-depthwise aggregation, are implemented to exploit and integrate the multichannel temporal embeddings. Finally, a classification network is used to predict EEG categories based on the integrated features. Our work is the first to explore the use of pretrained speech models for EEG signal analysis as well as the effective ways to integrate the multichannel temporal embeddings from the EEG signal. Extensive experimental results suggest that the proposed Speech2EEG method achieves state-of-the-art performance on two challenging motor imagery (MI) datasets, the BCI IV-2a and BCI IV-2b datasets, with accuracies of 89.5% and 84.07%, respectively. Visualization analysis of the multichannel temporal embeddings show that the Speech2EEG architecture can capture useful patterns related to MI categories, which can provide a novel solution for subsequent research under the constraints of a limited dataset scale.}, }
@article {pmid37079420, year = {2023}, author = {Liang, S and Kuang, S and Wang, D and Yuan, Z and Zhang, H and Sun, L}, title = {An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification with Limited Data.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3268979}, pmid = {37079420}, issn = {1558-0210}, abstract = {While deep learning algorithms significantly improves the decoding performance of brain-computer interface (BCI) based on electroencephalogram (EEG) signals, the performance relies on a large number of high-resolution data for training. However, collecting sufficient usable EEG data is difficult due to the heavy burden on the subjects and the high experimental cost. To overcome this data insufficiency, a novel auxiliary synthesis framework is first introduced in this paper, which composes of a pre-trained auxiliary decoding model and a generative model. The framework learns the latent feature distributions of real data and uses Gaussian noise to synthesize artificial data. The experimental evaluation reveals that the proposed method effectively preserves the time-frequency-spatial features of the real data and enhances the classification performance of the model using limited training data and is easy to implement, which outperforms the common data augmentation methods. The average accuracy of the decoding model designed in this work is improved by (4.72±0.98)% on the BCI competition IV 2a dataset. Furthermore, the framework is applicable to other deep learning-based decoders. The finding provides a novel way to generate artificial signals for enhancing classification performance when there are insufficient data, thus reducing data acquisition consuming in the BCI field.}, }
@article {pmid37077575, year = {2023}, author = {Chen, L and Lu, X and Jin, Q and Gao, Z and Wang, Y}, title = {Sensory innervation of the lumbar 5/6 intervertebral disk in mice.}, journal = {Frontiers in neurology}, volume = {14}, number = {}, pages = {1084209}, pmid = {37077575}, issn = {1664-2295}, abstract = {INTRODUCTION: Over the years, most back pain-related biological studies focused on the pathogenesis of disk degeneration. It is known that nerve distributions at the outer layer of the annulus fibrosus (AF) may be an important contributor to back pain symptoms. However, the types and origins of sensory nerve terminals in the mouse lumbar disks have not been widely studied. Using disk microinjection and nerve retrograde tracing methods, the current study aimed to characterize the nerve types and neuropathway of the lumbar 5/6 (L5/6) disk in mice.<br><br>METHODS: Using an anterior peritoneal approach, the L5/6 disk of adult C57BL/6 mice (males, 8-12 weeks) disk microinjection was performed. Fluorogold (FG) was injected into the L5/6 disk using the Hamilton syringe with a homemade glass needle driven by a pressure microinjector. The lumbar spine and bilateral thoracic 13 (Th13) to L6 DRGs were harvested at 10 days after injection. The number of FG[+] neurons among different levels was counted and analyzed. Different nerve markers, including anti-neurofilament 160/200 (NF160/200), anti-calcitonin gene-related peptide (CGRP), anti-parvalbumin (PV), and anti-tyrosine hydroxylase (TH), were used to identify different types of nerve terminals in AF and their origins in DRG neurons.<br><br>RESULTS: There were at least three types of nerve terminals at the outer layer of L5/6 AF in mice, including NF160/200[+] (indicating A&#x3b2; fibers), CGRP[+] (A&#x3b4; and C fibers), and PV[+] (proprioceptive fibers). No TH[+] fibers (sympathetic nerve fibers and some C-low threshold mechanoreceptors) were noticed in either. Using retrograde tracing methods, we found that nerve terminals in the L5/6 disk were multi-segmentally from Th13-L6 DRGs, with L1 and L5 predominately. An immunofluorescence analysis revealed that FG[+] neurons in DRGs were co-localized with NF160/200, CGRP, and PV, but not TH.<br><br>CONCLUSION: Intervertebral disks were innervated by multiple types of nerve fibers in mice, including A&#x3b2;, A&#x3b4;, C, and proprioceptive fibers. No sympathetic nerve fibers were found in AF. The nerve network of the L5/6 disk in mice was multi-segmentally innervated by the Th13-L6 DRGs (mainly L1 and L5 DRGs). Our results may serve as a reference for preclinical studies of discogenic pain in mice.}, }
@article {pmid37075914, year = {2023}, author = {Ma, J and Yang, B and Qiu, W and Zhang, J and Yan, L and Wang, W}, title = {Recognizable Rehabilitation Movements of Multiple Unilateral Upper Limb: an fMRI Study of Motor Execution and Motor Imagery.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109861}, doi = {10.1016/j.jneumeth.2023.109861}, pmid = {37075914}, issn = {1872-678X}, abstract = {BACKGROUND: This paper presents a study investigating the recognizability of multiple unilateral upper limb movements in stroke rehabilitation.<br><br>METHODS: A functional magnetic experiment is employed to study motor execution (ME) and motor imagery (MI) of four movements for the unilateral upper limb: hand-grasping, hand-handling, arm-reaching, and wrist-twisting. The functional magnetic resonance imaging (fMRI) images of ME and MI tasks are statistically analyzed to delineate the region of interest (ROI). Then parameter estimation associated with ROIs for each ME and MI task are evaluated, where differences in ROIs for different movements are compared using analysis of covariance (ANCOVA).<br><br>RESULTS: All movements of ME and MI tasks activate motor areas of the brain, and there are significant differences (p<0.05) in ROIs evoked by different movements. The activation area is larger when executing the hand-grasping task instead of the others.<br><br>CONCLUSION: The four movements we propose can be adopted as MI tasks, especially for stroke rehabilitation, since they are highly recognizable and capable of activating more brain areas during MI and ME.}, }
@article {pmid37074913, year = {2023}, author = {Yang, GM and Xu, L and Wang, RM and Tao, X and Zheng, ZW and Chang, S and Ma, D and Zhao, C and Dong, Y and Wu, S and Guo, J and Wu, ZY}, title = {Structures of the human Wilson disease copper transporter ATP7B.}, journal = {Cell reports}, volume = {42}, number = {5}, pages = {112417}, doi = {10.1016/j.celrep.2023.112417}, pmid = {37074913}, issn = {2211-1247}, abstract = {The P-type ATPase ATP7B exports cytosolic copper and plays an essential role in the regulation of cellular copper homeostasis. Mutants of ATP7B cause Wilson disease (WD), an autosomal recessive disorder of copper metabolism. Here, we present cryoelectron microscopy (cryo-EM) structures of human ATP7B in the E1 state in the apo, the putative copper-bound, and the putative cisplatin-bound forms. In ATP7B, the N-terminal sixth metal-binding domain (MBD6) binds at the cytosolic copper entry site of the transmembrane domain (TMD), facilitating the delivery of copper from the MBD6 to the TMD. The sulfur-containing residues in the TMD of ATP7B mark the copper transport pathway. By comparing structures of the E1 state human ATP7B and E2-Pi state frog ATP7B, we propose the ATP-driving copper transport model of ATP7B. These structures not only advance our understanding of the mechanisms of ATP7B-mediated copper export but can also guide the development of therapeutics for the treatment of WD.}, }
@article {pmid37074883, year = {2023}, author = {Zhao, S and Yang, J and Wang, J and Fang, C and Liu, T and Zhang, S and Sawan, M}, title = {A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2023.3268502}, pmid = {37074883}, issn = {1940-9990}, abstract = {Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency. 1) Hybrid neural network design: The processor supports artificial neural network (ANN)- and spiking neural network (SNN)-based neuromorphic processing where ANN is used to support the processing of ExG signals and SNN is used for handling neural spike signals. 2) Event-driven processing: The processor can perform always-on binary neural network (BNN)-based event detection with low-energy consumption, and it only switches to the high-accuracy convolutional neural network (CNN)-based recognition mode when events are detected. 3) Reconfigurable architecture: By exploiting the computational similarity of different neural networks, the processor supports critical BNN, CNN, and SNN operations with the same processing elements, achieving significant area reduction and energy efficiency improvement over those of a naive implementation. It achieves 90.05% accuracy and 4.38 uJ/class in a center-out reaching task with an SNN and 99.4% sensitivity, 98.6% specificity, and 1.93 uJ/class in an EEG-based seizure prediction task with dual neural network-based event-driven processing. Moreover, it achieves a classification accuracy of 99.92%, 99.38%, and 86.39% and energy consumption of 1.73, 0.99, and 1.31 uJ/class for EEG-based epileptic seizure detection, ECG-based arrhythmia detection, and EMG-based gesture recognition, respectively.}, }
@article {pmid37071937, year = {2023}, author = {Macías-Macías, JM and Ramírez-Quintana, JA and Chacón-Murguía, MI and Torres-García, AA and Corral-Martínez, LF}, title = {Interpretation of a deep analysis of speech imagery features extracted by a capsule neural network.}, journal = {Computers in biology and medicine}, volume = {159}, number = {}, pages = {106909}, doi = {10.1016/j.compbiomed.2023.106909}, pmid = {37071937}, issn = {1879-0534}, abstract = {Speech imagery has been successfully employed in developing Brain-Computer Interfaces because it is a novel mental strategy that generates brain activity more intuitively than evoked potentials or motor imagery. There are many methods to analyze speech imagery signals, but those based on deep neural networks achieve the best results. However, more research is necessary to understand the properties and features that describe imagined phonemes and words. In this paper, we analyze the statistical properties of speech imagery EEG signals from the KaraOne dataset to design a method that classifies imagined phonemes and words. With this analysis, we propose a Capsule Neural Network that categorizes speech imagery patterns into bilabial, nasal, consonant-vocal, and vowels/iy/ and/uw/. The method is called Capsules for Speech Imagery Analysis (CapsK-SI). The input of CapsK-SI is a set of statistical features of EEG speech imagery signals. The architecture of the Capsule Neural Network is composed of a convolution layer, a primary capsule layer, and a class capsule layer. The average accuracy reached is 90.88%±7 for bilabial, 90.15%±8 for nasal, 94.02%±6 for consonant-vowel, 89.70%±8 for word-phoneme, 94.33%± for/iy/ vowel and, 94.21%±3 for/uw/ vowel detection. Finally, with the activity vectors of the CapsK-SI capsules, we generated brain maps to represent brain activity in the production of bilabial, nasal, and consonant-vocal signals.}, }
@article {pmid37067278, year = {2023}, author = {Fallegger, F and Trouillet, A and Lacour, SP}, title = {Subdural Soft Electrocorticography (ECoG) Array Implantation and Long-Term Cortical Recording in Minipigs.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {193}, pages = {}, doi = {10.3791/64997}, pmid = {37067278}, issn = {1940-087X}, mesh = {Animals ; Humans ; Swine ; *Electrocorticography/methods ; Swine, Miniature ; *Brain/physiology ; Electrodes ; Evoked Potentials ; Electrodes, Implanted ; }, abstract = {Neurological impairments and diseases can be diagnosed or treated using electrocorticography (ECoG) arrays. In drug-resistant epilepsy, these help delineate the epileptic region to resect. In long-term applications such as brain-computer interfaces, these epicortical electrodes are used to record the movement intention of the brain, to control the robotic limbs of paralyzed patients. However, current stiff electrode grids do not answer the need for high-resolution brain recordings and long-term biointegration. Recently, conformable electrode arrays have been proposed to achieve long-term implant stability with high performance. However, preclinical studies for these new implant technologies are needed to validate their long-term functionality and safety profile for their translation to human patients. In this context, porcine models are routinely employed in developing medical devices due to their large organ sizes and easy animal handling. However, only a few brain applications are described in the literature, mostly due to surgery limitations and integration of the implant system on a living animal. Here, we report the method for long-term implantation (6 months) and evaluation of soft ECoG arrays in the minipig model. The study first presents the implant system, consisting of a soft microfabricated electrode array integrated with a magnetic resonance imaging (MRI)-compatible polymeric transdermal port that houses instrumentation connectors for electrophysiology recordings. Then, the study describes the surgical procedure, from subdural implantation to animal recovery. We focus on the auditory cortex as an example target area where evoked potentials are induced by acoustic stimulation. We finally describe a data acquisition sequence that includes MRI of the whole brain, implant electrochemical characterization, intraoperative and freely moving electrophysiology, and immunohistochemistry staining of the extracted brains. This model can be used to investigate the safety and function of novel design of cortical prostheses; mandatory preclinical study to envision translation to human patients.}, }
@article {pmid37067101, year = {2023}, author = {Zhang, J and Huang, Y and Jiang, C and Xu, Y and Rao, H and Xu, H}, title = {Dynamic brain responses to Russian word acquisition among Chinese adult learners: An event-related potential study.}, journal = {Human brain mapping}, volume = {}, number = {}, pages = {}, doi = {10.1002/hbm.26307}, pmid = {37067101}, issn = {1097-0193}, abstract = {Human learners are capable to acquire foreign language vocabulary at an impressive speed even in adulthood. Previous studies have examined the neural mechanisms underlying rapid acquisition of Latin-alphabet vocabulary and revealed dynamic changes in several event-related potential (ERP) components during novel word learning. However, scant attention has been paid to the acquisition of Russian words. The present study used ERP and examined dynamic brain responses to rapid Russian word acquisition in 53 native Chinese speakers with no prior knowledge of Russian language. Behavioral data showed robust individual differences in Russian word acquisition, with most participants being able to rapidly learn a subset of novel Russian words in a few exposures. ERP results revealed significant learning effects in the P200, N400, and P600 amplitudes. Moreover, P600 amplitude changes predicted participants' word acquisition after learning. These findings demonstrated dynamic brain responses to rapid Russian word learning and suggested that the P600 component may serve as a bio-marker for individual learning ability in Russian word acquisition.}, }
@article {pmid37065928, year = {2023}, author = {Qi, Y and Sun, Y and Liu, Q and Zhang, Q and Cai, H and Zheng, Q}, title = {Editorial: The intersection of artificial intelligence and brain for high-performance neuroprosthesis and cyborg systems.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1133002}, pmid = {37065928}, issn = {1662-4548}, }
@article {pmid37064914, year = {2023}, author = {Arif, S and Munawar, S and Ali, H}, title = {Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.}, journal = {Frontiers in physiology}, volume = {14}, number = {}, pages = {1153268}, pmid = {37064914}, issn = {1664-042X}, abstract = {Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks. Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics. Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen's kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods. Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.}, }
@article {pmid37063105, year = {2023}, author = {Gwon, D and Won, K and Song, M and Nam, CS and Jun, SC and Ahn, M}, title = {Review of public motor imagery and execution datasets in brain-computer interfaces.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1134869}, pmid = {37063105}, issn = {1662-5161}, abstract = {The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.}, }
@article {pmid37062374, year = {2023}, author = {Kieffaber, PD and Osborne, J and Norton, E and Hilimire, M}, title = {Deconstructing the Functional Significance of the Error-related Negativity (ERN) and Midline Frontal Theta Oscillations Using Stepwise Time-locking and Single-trial Response Dynamics.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120113}, doi = {10.1016/j.neuroimage.2023.120113}, pmid = {37062374}, issn = {1095-9572}, abstract = {Error-related electroencephalographic potentials have been used for decades to develop theoretical models of response monitoring processes, study altered cognitive functioning in clinical populations, and more recently, to improve the performance of brain-computer interfaces. However, the vast majority of this research relies on discrete behavioral responses that confound error detection, response cancellation, error correction, and post-error cognitive and affective processes. By contrast, the present study demonstrates a novel, complementary method for isolating the functional correlates of error-related electroencephalographic responses using single-trial kinematic analyses of cursor trajectories and a stepwise time-locking analysis. The results reveal that the latency of the ERN, Pe, and medial-frontal theta oscillations are all strongly positively correlated with the latency at which an initiated error response is canceled, as indicated by the peak deceleration of the initiated movement prior to a corrective response. Results are discussed with respect to current theoretical models of error-related brain potentials and potential relevance to clinical applications.}, }
@article {pmid37062178, year = {2023}, author = {Li, J and Wang, F and Huang, H and Qi, F and Pan, J}, title = {A novel semi-supervised meta learning method for subject-transfer brain-computer interface.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {163}, number = {}, pages = {195-204}, doi = {10.1016/j.neunet.2023.03.039}, pmid = {37062178}, issn = {1879-2782}, abstract = {The brain-computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications.}, }
@article {pmid37061673, year = {2023}, author = {Fu, J and Jiang, Z and Shu, X and Chen, S and Jia, J}, title = {Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study.}, journal = {Biomedical engineering online}, volume = {22}, number = {1}, pages = {36}, pmid = {37061673}, issn = {1475-925X}, abstract = {BACKGROUND AND AIMS: Brain-computer interfaces (BCIs) are emerging as a promising tool for upper limb recovery after stroke, and motor tasks are an essential part of BCIs for patient training and control of rehabilitative/assistive BCIs. However, the correlation between brain activation with different levels of motor impairment and motor tasks in BCIs is still not so clear. Thus, we aim to compare the brain activation of different levels of motor impairment in performing the hand grasping and opening tasks in BCIs.<br><br>METHODS: We instructed stroke patients to perform motor attempts (MA) to grasp and open the affected hand for 30 trials, respectively. During this period, they underwent EEG acquisition and BCIs accuracy recordings. They also received detailed history records and behavioral scale assessments (the Fugl-Meyer assessment of upper limb, FMA-UE).<br><br>RESULTS: The FMA-UE was negatively correlated with the event-related desynchronization (ERD) of the affected hemisphere during open MA (R&#x2009;=&#x2009;-&#xa0;0.423, P&#x2009;=&#x2009;0.009) but not with grasp MA (R&#x2009;=&#x2009;-&#xa0;0.058, P&#x2009;=&#x2009;0.733). Then we divided the stroke patients into group 1 (Brunnstrom recovery stages between I to II, n&#x2009;=&#x2009;19) and group 2 (Brunnstrom recovery stages between III to VI, n&#x2009;=&#x2009;23). No difference during the grasping task (t&#x2009;=&#x2009;0.091, P&#x2009;=&#x2009;0.928), but a significant difference during the open task (t&#x2009;=&#x2009;2.156, P&#x2009;=&#x2009;0.037) was found between the two groups on the affected hemisphere. No significant difference was found in the unaffected hemisphere.<br><br>CONCLUSIONS: The study indicated that brain activation is positively correlated with the hand function of stroke in open-hand tasks. In the grasping task, the patients in the different groups have a similar brain response, while in the open task, mildly injured patients have more brain activation in open the hand than the poor hand function patients.}, }
@article {pmid37061103, year = {2023}, author = {Ma, Z and Li, W and Zhuang, L and Wen, T and Wang, P and Yu, H and Liu, Y and Yu, Y}, title = {TMEM59 ablation leads to loss of olfactory sensory neurons and impairs olfactory functions via interaction with inflammation.}, journal = {Brain, behavior, and immunity}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.bbi.2023.04.005}, pmid = {37061103}, issn = {1090-2139}, abstract = {The olfactory epithelium undergoes constant neurogenesis throughout life in mammals. Several factors including key signaling pathways and inflammatory microenvironment regulate the maintenance and regeneration of the olfactory epithelium. In this study, we identify TMEM59 (also known as DCF1) as a critical regulator to the epithelial maintenance and regeneration. Single-cell RNA-Seq data show downregulation of TMEM59 in multiple epithelial cell lineages with aging. Ablation of TMEM59 leads to apparent alteration at transcriptional level, including genes associated with olfactory transduction and inflammatory/immune response. These differentially expressed genes are key components belonging to several signaling pathways, such as NF-κB, chemokine, etc. TMEM59 deletion impairs olfactory functions, attenuates proliferation, causes loss of both mature and immature olfactory sensory neurons, and promotes infiltration of inflammatory cells, macrophages, microglia cells and neutrophils into the olfactory epithelium and lamina propria. TMEM59 deletion deteriorates regeneration of the olfactory epithelium after injury, with significant reduction in the number of proliferative cells, immature and mature sensory neurons, accompanied by the increasing number of inflammatory cells and macrophages. Anti-inflammation by dexamethasone recovers neuronal generation and olfactory functions in the TMEM59-KO animals, suggesting the correlation between TMEM59 and inflammation in regulating the epithelial maintenance. Collectively, TMEM59 regulates olfactory functions, as well as neuronal generation in the olfactory epithelium via interaction with inflammation, suggesting a potential role in therapy against olfactory dysfunction associated with inflamm-aging.}, }
@article {pmid37059125, year = {2023}, author = {Hosseini, SM and Aminitabar, AH and Shalchyan, V}, title = {Investigating the Application of Graph Theory Features in Hand Movement Directions Decoding using EEG Signals.}, journal = {Neuroscience research}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neures.2023.04.002}, pmid = {37059125}, issn = {1872-8111}, abstract = {In recent years, functional analysis of brain networks based on graph theory properties has attracted considerable attention. This approach has usually been exploited for structural and functional brain analysis, while its potential in motor decoding tasks has remained unexplored. This study aimed to investigate the feasibility of using graph-based features in hand direction decoding in movement execution and preparation intervals. Hence, EEG signals were recorded from nine healthy subjects while performing a four-target center-out reaching task. The functional brain network was calculated based on the magnitude squared coherence (MSC) at six frequency bands. Then, the features based on eight graph theory metrics were extracted from brain networks. The classification was performed with a support vector machine classifier. The results revealed that in four-class direction discrimination, the mean accuracy of the graph-based method surpassed 63% and 53% on movement and pre-movement data, respectively. Additionally, a feature fusion approach that combines the graph theory features with power features was proposed. The fusion method raised the classification accuracy to 70.8% and 61.2% for movement and pre-movement intervals, respectively. This work has verified the feasibility of using graph theory properties and their superiority over band power features in a hand movement decoding task.}, }
@article {pmid37056962, year = {2023}, author = {Long, T and Wan, M and Jian, W and Dai, H and Nie, W and Xu, J}, title = {Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1143027}, pmid = {37056962}, issn = {1662-5161}, abstract = {INTRODUCTION: The volume conduction effect and high dimensional characteristics triggered by the excessive number of channels of EEG cap-acquired signals in BCI systems can increase the difficulty of classifying EEG signals and the lead time of signal acquisition. We aim to combine transfer learning to decode EEG signals in the few-channel case, improve the classification performance of the motor imagery BCI system across subject cases, reduce the cost of signal acquisition performed by the BCI system, and improve the usefulness of the system.<br><br>METHODS: Dataset2a from BCI CompetitionIV(2008) was used as Dataset1, and our team's self-collected dataset was used as Dataset2. Dataset1 acquired EEG signals from 9 subjects using a 22-channel device with a sampling frequency of 250 Hz. Dataset2 acquired EEG signals from 10 healthy subjects (8 males and 2 females; age distribution between 21-30 years old; mean age 25 years old) using an 8-channel system with a sampling frequency of 1000 Hz. We introduced EA in the data preprocessing process to reduce the signal differences between subjects and proposed VFB-RCSP in combination with RCSP and FBCSP to optimize the effect of feature extraction.<br><br>RESULTS: Experiments were conducted on Dataset1 with EEG data containing only 8 channels and achieved an accuracy of 78.01 and a kappa coefficient of 0.54. The accuracy exceeded most of the other methods proposed in recent years, even though the number of channels used was significantly reduced. On Dataset 2, an accuracy of 59.77 and a Kappa coefficient of 0.34 were achieved, which is a significant improvement compared to other poorly improved classical protocols.<br><br>DISCUSSION: Our work effectively improves the classification of few-channel EEG data. It overcomes the dependence of existing algorithms on the number of channels, the number of samples, and the frequency band, which is significant for reducing the complexity of BCI models and improving the user-friendliness of BCI systems.}, }
@article {pmid37056480, year = {2023}, author = {Jain, P and Conte, MM and Voss, HU and Victor, JD and Schiff, ND}, title = {Low-level language processing in brain-injured patients.}, journal = {Brain communications}, volume = {5}, number = {2}, pages = {fcad094}, pmid = {37056480}, issn = {2632-1297}, abstract = {Assessing cognitive function-especially language processing-in severely brain-injured patients is critical for prognostication, care, and development of communication devices (e.g. brain-computer interfaces). In patients with diminished motor function, language processing has been probed using EEG measures of command-following in motor imagery tasks. While such tests eliminate the need for motor response, they require sustained attention. However, passive listening tasks, with an EEG response measure can reduce both motor and attentional demands. These considerations motivated the development of two assays of low-level language processing-identification of differential phoneme-class responses and tracking of the natural speech envelope. This cross-sectional study looks at a cohort of 26 severely brain-injured patient subjects and 10 healthy controls. Patients' level of function was assessed via the coma recovery scale-revised at the bedside. Patients were also tested for command-following via EEG and/or MRI assays of motor imagery. For the present investigation, EEG was recorded while presenting a 148 s audio clip of Alice in Wonderland. Time-locked EEG responses to phoneme classes were extracted and compared to determine a differential phoneme-class response. Tracking of the natural speech envelope was assessed from the same recordings by cross-correlating the EEG response with the speech envelope. In healthy controls, the dynamics of the two measures were temporally similar but spatially different: a central parieto-occipital component of differential phoneme-class response was absent in the natural speech envelope response. The differential phoneme-class response was present in all patient subjects, including the six classified as vegetative state/unresponsive wakefulness syndrome by behavioural assessment. However, patient subjects with evidence of language processing either by behavioural assessment or motor imagery tests had an early bilateral response in the first 50 ms that was lacking in patient subjects without any evidence of language processing. The natural speech envelope tracking response was also present in all patient subjects and responses in the first 100 ms distinguished patient subjects with evidence of language processing. Specifically, patient subjects with evidence of language processing had a more global response in the first 100 ms whereas those without evidence of language processing had a frontopolar response in that period. In summary, we developed two passive EEG-based methods to probe low-level language processing in severely brain-injured patients. In our cohort, both assays showed a difference between patient subjects with evidence of command-following and those with no evidence of command-following: a more prominent early bilateral response component.}, }
@article {pmid37052377, year = {2023}, author = {Yong, X and Lu, S and Hsu, YC and Fu, C and Sun, Y and Zhang, Y}, title = {Numerical fitting of Extrapolated semisolid Magnetization transfer Reference signals: Improved detection of ischemic stroke.}, journal = {Magnetic resonance in medicine}, volume = {}, number = {}, pages = {}, doi = {10.1002/mrm.29660}, pmid = {37052377}, issn = {1522-2594}, abstract = {PURPOSE: To propose a novel Numerical fitting method of the Extrapolated semisolid Magnetization transfer Reference (NEMR) signal for quantifying the CEST effect.<br><br>THEORY AND METHODS: Modified two-pool Bloch-McConnell equations were used to numerically fit the magnetization transfer (MT) and direct water saturation (DS) signals at far off-resonance frequencies, which was subsequently extrapolated into the frequency range of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) pools. Then the subtraction of the fitted two-pool z-spectrum and the experimentally acquired z-spectrum yielded APT[#] and NOE[#] signals mostly free of MT and DS contamination. Several strategies were used to accelerate the NEMR fitting. Furthermore, the proposed NEMR method was compared with the conventional extrapolated semisolid magnetization transfer reference (EMR) and magnetization transfer ratio asymmetry (MTRasym) methods in simulations and stroke patients.<br><br>RESULTS: The combination of RF downsampling, MT lineshape look-up table, and conversion of MATLAB code to C code accelerated the NEMR fitting by over 2700-fold. Monte-Carlo simulations showed that NEMR had higher accuracy than EMR and eliminated the requirement of the steady-state condition. In ischemic stroke patients, the NEMR maps at 1&#x2009;&#x3bc;T removed hypointense artifacts seen on EMR and MTRasym images, and better depicted stroke lesions than EMR. For NEMR, NOE[#] yielded significantly (p&#x2009;<&#x2009;0.05) stronger signal contrast between stroke and normal tissues than APT[#] at 1&#x2009;&#x3bc;T.<br><br>CONCLUSION: The proposed NEMR method is suitable for arbitrary saturation settings and can remove MT and DS contamination from the CEST signal for improved detection of ischemic stroke.}, }
@article {pmid37051328, year = {2023}, author = {Guo, R and Lin, Y and Luo, X and Gao, X and Zhang, S}, title = {A robotic arm control system with simultaneous and sequential modes combining eye-tracking with steady-state visual evoked potential in virtual reality environment.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1146415}, pmid = {37051328}, issn = {1662-5218}, abstract = {At present, single-modal brain-computer interface (BCI) still has limitations in practical application, such as low flexibility, poor autonomy, and easy fatigue for subjects. This study developed an asynchronous robotic arm control system based on steady-state visual evoked potentials (SSVEP) and eye-tracking in virtual reality (VR) environment, including simultaneous and sequential modes. For simultaneous mode, target classification was realized by decision-level fusion of electroencephalography (EEG) and eye-gaze. The stimulus duration for each subject was non-fixed, which was determined by an adjustable window method. Subjects could autonomously control the start and stop of the system using triple blink and eye closure, respectively. For sequential mode, no calibration was conducted before operation. First, subjects' gaze area was obtained through eye-gaze, and then only few stimulus blocks began to flicker. Next, target classification was determined using EEG. Additionally, subjects could reject false triggering commands using eye closure. In this study, the system effectiveness was verified through offline experiment and online robotic-arm grasping experiment. Twenty subjects participated in offline experiment. For simultaneous mode, average ACC and ITR at the stimulus duration of 0.9 s were 90.50% and 60.02 bits/min, respectively. For sequential mode, average ACC and ITR at the stimulus duration of 1.4 s were 90.47% and 45.38 bits/min, respectively. Fifteen subjects successfully completed the online tasks of grabbing balls in both modes, and most subjects preferred the sequential mode. The proposed hybrid brain-computer interface (h-BCI) system could increase autonomy, reduce visual fatigue, meet individual needs, and improve the efficiency of the system.}, }
@article {pmid37050823, year = {2023}, author = {Cardona-Álvarez, YN and Álvarez-Meza, AM and Cárdenas-Peña, DA and Castaño-Duque, GA and Castellanos-Dominguez, G}, title = {A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {7}, pages = {}, pmid = {37050823}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Software ; Imagery, Psychotherapy ; Electrodes ; }, abstract = {An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.}, }
@article {pmid37050774, year = {2023}, author = {Zafar, A and Hussain, SJ and Ali, MU and Lee, SW}, title = {Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {7}, pages = {}, pmid = {37050774}, issn = {1424-8220}, mesh = {*Imagination ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Imagery, Psychotherapy ; Algorithms ; Electroencephalography/methods ; }, abstract = {In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.}, }
@article {pmid37050653, year = {2023}, author = {Srisrisawang, N and Müller-Putz, GR}, title = {Transfer Learning in Trajectory Decoding: Sensor or Source Space?.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {7}, pages = {}, doi = {10.3390/s23073593}, pmid = {37050653}, issn = {1424-8220}, support = {ERC-CoG-2015 681231/ERC_/European Research Council/International ; }, abstract = {In this study, across-participant and across-session transfer learning was investigated to minimize the calibration time of the brain-computer interface (BCI) system in the context of continuous hand trajectory decoding. We reanalyzed data from a study with 10 able-bodied participants across three sessions. A leave-one-participant-out (LOPO) model was utilized as a starting model. Recursive exponentially weighted partial least squares regression (REW-PLS) was employed to overcome the memory limitation due to the large pool of training data. We considered four scenarios: generalized with no update (Gen), generalized with cumulative update (GenC), and individual models with cumulative (IndC) and non-cumulative (Ind) updates, with each one trained with sensor-space features or source-space features. The decoding performance in generalized models (Gen and GenC) was lower than the chance level. In individual models, the cumulative update (IndC) showed no significant improvement over the non-cumulative model (Ind). The performance showed the decoder's incapability to generalize across participants and sessions in this task. The results suggested that the best correlation could be achieved with the sensor-space individual model, despite additional anatomical information in the source-space features. The decoding pattern showed a more localized pattern around the precuneus over three sessions in Ind models.}, }
@article {pmid37050605, year = {2023}, author = {Borkin, D and Nemethova, A and Nemeth, M and Tanuska, P}, title = {Control of a Production Manipulator with the Use of BCI in Conjunction with an Industrial PLC.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {7}, pages = {}, doi = {10.3390/s23073546}, pmid = {37050605}, issn = {1424-8220}, abstract = {Research in the field of gathering and analyzing biological signals is growing. The sensors are becoming more available and more non-invasive for examining such signals, which in the past required the inconvenient acquisition of data. This was achieved mainly by the fact that biological sensors were able to be built into wearable and portable devices. The representation and analysis of EEGs (electroencephalograms) is nowadays commonly used in various application areas. The application of the use of the EEG signals to the field of automation is still an unexplored area and therefore provides opportunities for interesting research. In our research, we focused on the area of processing automation; especially the use of the EEG signals to bridge the communication between control of individual processes and a human. In this study, the real-time communication between a PLC (programmable logic controller) and BCI (brain computer interface) was investigated and described. In the future, this approach can help people with physical disabilities to control certain machines or devices and therefore it could find applicability in overcoming physical disabilities. The main contribution of the article is, that we have demonstrated the possibility of interaction between a person and a manipulator controlled by a PLC with the help of a BCI. Potentially, with the expansion of functionality, such solutions will allow a person with physical disabilities to participate in the production process.}, }
@article {pmid37046941, year = {2023}, author = {Gao, S and Zhou, K and Zhang, J and Cheng, Y and Mao, S}, title = {Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs.}, journal = {Healthcare (Basel, Switzerland)}, volume = {11}, number = {7}, pages = {}, doi = {10.3390/healthcare11071014}, pmid = {37046941}, issn = {2227-9032}, abstract = {As a widely used brain-computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants' mental fatigue. The study's results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants' mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users' mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.}, }
@article {pmid37046310, year = {2023}, author = {Perez-Valero, E and Gutierrez, CAM and Lopez-Gordo, MA and Alcalde, SL}, title = {Evaluating the feasibility of cognitive impairment detection in Alzheimer's disease screening using a computerized visual dynamic test.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {43}, pmid = {37046310}, issn = {1743-0003}, abstract = {BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease without known cure. However, early medical treatment can help control its progression and postpone intellectual decay. Since AD is preceded by a period of cognitive deterioration, the effective assessment of cognitive capabilities is crucial to develop reliable screening procedures. For this purpose, cognitive tests are extensively used to evaluate cognitive areas such as language, attention, or memory.<br><br>METHODS: In this work, we analyzed the potential of a visual dynamics evaluation, the rapid serial visual presentation task (RSVP), for the detection of cognitive impairment in AD. We compared this evaluation with two of the most extended brief cognitive tests applied in Spain: the Clock-drawing test (CDT) and the Phototest. For this purpose, we assessed a group of patients (mild AD and mild cognitive impairment) and controls, and we evaluated the ability of the three tests for the discrimination of the two groups.<br><br>RESULTS: The preliminary results obtained suggest the RSVP performance is statistically higher for the controls than for the patients (p-value = 0.013). Furthermore, we obtained promising classification results for this test (mean accuracy of 0.91 with 95% confidence interval 0.72, 0.97).<br><br>CONCLUSIONS: Since the RSVP is a computerized, auto-scored, and potentially self-administered brief test, it could contribute to speeding-up cognitive impairment screening and to reducing the associated costs. Furthermore, this evaluation could be combined with other tests to augment the efficiency of cognitive impairment screening protocols and to potentially monitor patients under medical treatment.}, }
@article {pmid37044093, year = {2023}, author = {Bonizzato, M and Guay Hottin, R and Côté, SL and Massai, E and Choinière, L and Macar, U and Laferrière, S and Sirpal, P and Quessy, S and Lajoie, G and Martinez, M and Dancause, N}, title = {Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.}, journal = {Cell reports. Medicine}, volume = {}, number = {}, pages = {101008}, doi = {10.1016/j.xcrm.2023.101008}, pmid = {37044093}, issn = {2666-3791}, abstract = {Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged neurostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve "prior" expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.}, }
@article {pmid37043349, year = {2023}, author = {Yao, T and Vanduffel, W}, title = {Spike rates of frontal eye field neurons predict reaction times in a spatial attention task.}, journal = {Cell reports}, volume = {42}, number = {4}, pages = {112384}, doi = {10.1016/j.celrep.2023.112384}, pmid = {37043349}, issn = {2211-1247}, abstract = {Which neuronal signal(s) predict reaction times when subjects respond to a target at covertly attended locations? Although recent studies showed that spike rates are not predictive, it remains a highly contested question. Therefore, we record single-unit activity from frontal eye field (FEF) neurons while macaques are performing a covert spatial attention task. We find that the attentional modulation of spike rates of FEF neurons is strongly correlated with behavioral reaction times. Moreover, this correlation already emerges 1 s before target dimming, which triggers the behavioral responses. This prediction of reaction times by spike rates is found in neurons showing attention-dependent enhanced and suppressed activity for targets and distractors, respectively, yet in varying degrees across subjects. Thus, spike rates of FEF neurons can predict reaction times persistently and well before the operant behavior during selective attention tasks. Such long prediction windows will be useful for developing spike-based brain-machine interfaces.}, }
@article {pmid37043313, year = {2023}, author = {Xia, M and Chen, C and Xu, Y and Li, Y and Sheng, X and Ding, H}, title = {Extracting Individual Muscle Drive and Activity From High-Density Surface Electromyography Signals Based on the Center of Gravity of Motor Unit.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3266575}, pmid = {37043313}, issn = {1558-2531}, abstract = {Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface electromyogram (EMG) in studying the neural control of movement and proposed a new non-invasive way of extracting neural drive to individual muscles. Sixteen subjects performed isometric contractions to complete six hand tasks. High-density surface EMG signals (256 channels in total) recorded from the forearm muscles were decomposed into motor unit firing trains. The location of each decomposed motor unit was represented by its center of gravity and was put into clustering for distinct muscle regions. All the motor units in the same cluster served as a muscle-specific motor pool from which individual muscle drive could be extracted directly. Moreover, we cross-validated the self-clustered muscle regions by magnetic resonance imaging (MRI) recorded from the subjects' forearms. All motor units that fall within the MRI region are considered correctly clustered. We achieved a clustering accuracy of 95.72% 4.01% for all subjects. We provided a new framework for collecting experimental muscle-specific drives and generalized the way of surface electrode placement without prior knowledge of the targeting muscle architecture.}, }
@article {pmid37040738, year = {2023}, author = {Chiang, KJ and Dong, S and Cheng, CK and Jung, TP}, title = {Using EEG signals to assess workload during memory retrieval in a real-world scenario.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/accbed}, pmid = {37040738}, issn = {1741-2552}, abstract = {The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during participants' typical office tasks on a single-monitor and dual-monitor arrangement. We expect a higher memory workload for the single-monitor arrangement. Approach: We designed an experiment that mimics the scenario of a subject performing some office work and examined whether the subjects experienced various levels of memory workload in two different office setups: 1) a single-monitor setup and 2) a dual-monitor setup. We used EEG band power, mutual information, and coherence as features to train machine learning models to classify high versus low memory workload states. Main results: The study results showed that these characteristics exhibited significant differences that were consistent across all participants. We also verified the robustness and consistency of these EEG signatures in a different data set collected during a Sternberg task in a prior study. Significance: The study found the EEG correlates of memory workload across individuals, demonstrating the effectiveness of using EEG analysis in conducting real-world neuroergonomic studies.}, }
@article {pmid37038142, year = {2023}, author = {Habashi, AG and Azab, AM and Eldawlatly, S and Aly, GM}, title = {Generative adversarial networks in EEG analysis: an overview.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {40}, pmid = {37038142}, issn = {1743-0003}, mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Image Processing, Computer-Assisted/methods ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; }, abstract = {Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.}, }
@article {pmid37035943, year = {2023}, author = {Duan, Y and Wang, S and Yuan, Q and Shi, Y and Jiang, N and Jiang, D and Song, J and Wang, P and Zhuang, L}, title = {Long-Term Flexible Neural Interface for Synchronous Recording of Cross-Regional Sensory Processing along the Olfactory Pathway.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e2205768}, doi = {10.1002/smll.202205768}, pmid = {37035943}, issn = {1613-6829}, abstract = {Humans perceive the world through five senses, of which olfaction is the oldest evolutionary sense that enables the detection of chemicals in the external environment. Recent progress in bioinspired electronics has boosted the development of artificial sensory systems. Here, a biohybrid olfactory system is proposed by integrating living mammals with implantable flexible neural electrodes, to employ the outstanding properties of mammalian olfactory system. In olfactory perception, the peripheral organ-olfactory epithelium (OE) projects axons into the olfactory relay station-olfactory bulb (OB). The olfactory information encoded in the neural activity is recorded from both OE and OB simultaneously using flexible neural electrodes. Results reveal that spontaneous slow oscillations (<12 Hz) in both OE and OB closely follow respiration. This respiration-locked rhythm modulates the amplitude of fast oscillations (>20 Hz), which are associated with odor perception. Further, by extracting the characteristics of odor-evoked oscillatory signals, responses of different odors are identified and classified with 80% accuracy. This study demonstrates for the first time that the flexible electrode enables chronic stable electrophysiological recordings of the peripheral and central olfactory system in vivo. Overall, the method provides a novel neural interface for olfactory biosensing and cognitive processing.}, }
@article {pmid37034665, year = {2023}, author = {Stout, JJ and George, AE and Kim, S and Hallock, HL and Griffin, AL}, title = {Harnessing prefrontal-hippocampal theta synchrony to enhance memory-guided choice.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.04.02.535279}, pmid = {37034665}, abstract = {Working memory is correlated with prefrontal-hippocampal oscillatory synchrony, but whether endogenous patterns of synchronized brain rhythms can be used to bias future choice remains unknown. Here, we developed a brain machine interface that detected states of strong and weak theta synchrony for task and neural manipulation. States of strong prefrontal-hippocampal theta coherence were characterized by strengthened prefrontal theta rhythms and were used to enhance memory-guided choice. In follow up experiments and analyses, we show that strong prefrontal-hippocampal theta coherence was associated with task engagement, phase modulation of prefrontal neurons to ventral midline thalamic theta, and heightened excitability in a select group of neurons. Through optogenetic manipulation of the ventral midline thalamus, we produced prefrontal theta rhythms and enhanced prefrontal-hippocampal oscillatory synchrony. These experiments show that prefrontal-hippocampal oscillatory synchrony can be used to bias memory-guided choices and provide evidence in support of the communication through coherence hypothesis.}, }
@article {pmid37034169, year = {2023}, author = {Wan, Z and Li, M and Liu, S and Huang, J and Tan, H and Duan, W}, title = {EEGformer: A transformer-based brain activity classification method using EEG signal.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1148855}, pmid = {37034169}, issn = {1662-4548}, abstract = {BACKGROUND: The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain.<br><br>METHOD: Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG).<br><br>RESULTS: The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance.<br><br>CONCLUSION: EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.}, }
@article {pmid37034156, year = {2023}, author = {Li, Y and Yang, B and Wang, Z and Huang, R and Lu, X and Bi, X and Zhou, S}, title = {EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1133834}, pmid = {37034156}, issn = {1662-4548}, abstract = {In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of "chronic primary pain" (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.}, }
@article {pmid37033911, year = {2023}, author = {Belkacem, AN and Jamil, N and Khalid, S and Alnajjar, F}, title = {On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1085173}, pmid = {37033911}, issn = {1662-5161}, abstract = {Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.}, }
@article {pmid37033908, year = {2023}, author = {Savić, AM and Novičić, M and Ðorđević, O and Konstantinović, L and Miler-Jerković, V}, title = {Novel electrotactile brain-computer interface with somatosensory event-related potential based control.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1096814}, pmid = {37033908}, issn = {1662-5161}, abstract = {OBJECTIVE: A brain computer interface (BCI) allows users to control external devices using non-invasive brain recordings, such as electroencephalography (EEG). We developed and tested a novel electrotactile BCI prototype based on somatosensory event-related potentials (sERP) as control signals, paired with a tactile attention task as a control paradigm.<br><br>APPROACH: A novel electrotactile BCI comprises commercial EEG device, an electrical stimulator and custom software for EEG recordings, electrical stimulation control, synchronization between devices, signal processing, feature extraction, selection, and classification. We tested a novel BCI control paradigm based on tactile attention on a sensation at a target stimulation location on the forearm. Tactile stimuli were electrical pulses delivered at two proximal locations on the user's forearm for stimulating branches of radial and median nerves, with equal probability of the target and distractor stimuli occurrence, unlike in any other ERP-based BCI design. We proposed a compact electrical stimulation electrodes configuration for delivering electrotactile stimuli (target and distractor) using 2 stimulation channels and 3 stimulation electrodes. We tested the feasibility of a single EEG channel BCI control, to determine pseudo-online BCI performance, in ten healthy subjects. For optimizing the BCI performance we compared the results for two classifiers, sERP averaging approaches, and novel dedicated feature extraction/selection methods via cross-validation procedures.<br><br>MAIN RESULTS: We achieved a single EEG channel BCI classification accuracy in the range of 75.1 to 88.1% for all subjects. We have established an optimal combination of: single trial averaging to obtain sERP, feature extraction/selection methods and classification approach.<br><br>SIGNIFICANCE: The obtained results demonstrate that a novel electrotactile BCI paradigm with equal probability of attended (target) and unattended (distractor) stimuli and proximal stimulation sites is feasible. This method may be used to drive restorative BCIs for sensory retraining in stroke or brain injury, or assistive BCIs for communication in severely disabled users.}, }
@article {pmid37030758, year = {2023}, author = {Li, A and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H}, title = {MDTL: A Novel and Model-Agnostic Transfer Learning Strategy for Cross-Subject Motor Imagery BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3259730}, pmid = {37030758}, issn = {1558-0210}, abstract = {In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learning on different datasets and models is still challenging. In this paper, we proposed a multi-direction transfer learning (MDTL) strategy for cross-subject MI EEG-based BCI. This strategy utilizes data from multi-source domains to the target domain as well as from one multi-source domain to another multi-source domain. This strategy is model-independent so that it can be quickly deployed on existing models. Three generic deep learning models for MI classification (DeepConvNet, ShallowConvNet, and EEGNet) and two public motor imagery datasets (BCIC IV dataset 2a and Lee2019) are used in this study to verify the proposed strategy. For the four-classes dataset BCIC IV dataset 2a, the proposed MDTL achieves 80.86%, 81.95%, and 75.00% mean prediction accuracy using the three models, which outperforms those without MDTL by 5.79%, 6.64%, and 11.42%. For the binary-classes dataset Lee2019, MDTL achieves 88.2% mean accuracy using the model DeepConvNet. It outperforms the accuracy without MDTL by 23.48%. The achieved 81.95% and 88.2% are also better than the existing deep transfer learning strategy. Besides, the training time of MDTL is reduced by 93.94%. MDTL is an easy-to-deploy, scalable and reliable transfer learning strategy for existing deep learning models, which significantly improves model performance and reduces preparation time without changing model architecture.}, }
@article {pmid37030737, year = {2023}, author = {Zhang, R and Cao, L and Xu, Z and Zhang, Y and Zhang, L and Hu, Y and Chen, M and Yao, D}, title = {Improving AR-SSVEP Recognition Accuracy Under High Ambient Brightness Through Iterative Learning.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {1796-1806}, doi = {10.1109/TNSRE.2023.3260842}, pmid = {37030737}, issn = {1558-0210}, abstract = {Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities (600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments.}, }
@article {pmid37030734, year = {2023}, author = {Gao, W and Huang, W and Li, M and Gu, Z and Pan, J and Yu, T and Yu, ZL and Li, Y}, title = {Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {1754-1763}, doi = {10.1109/TNSRE.2023.3259991}, pmid = {37030734}, issn = {1558-0210}, abstract = {A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.}, }
@article {pmid37030724, year = {2023}, author = {Liu, D and Dai, W and Zhang, H and Jin, X and Cao, J and Kong, W}, title = {Brain-Machine Coupled Learning Method for Facial Emotion Recognition.}, journal = {IEEE transactions on pattern analysis and machine intelligence}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TPAMI.2023.3257846}, pmid = {37030724}, issn = {1939-3539}, abstract = {Neural network models of machine learning have shown promising prospects for visual tasks, such as facial emotion recognition (FER). However, the generalization of the model trained from a dataset with a few samples is limited. Unlike the machine, the human brain can effectively realize the required information from a few samples to complete the visual tasks. To learn the generalization ability of the brain, in this paper, we propose a novel brain-machine coupled learning method for facial emotion recognition to let the neural network learn the visual knowledge of the machine and cognitive knowledge of the brain simultaneously. The proposed method utilizes visual images and electroencephalogram (EEG) signals to couple training the models in the visual and cognitive domains. Each domain model consists of two types of interactive channels, common and private. Since the EEG signals can reflect brain activity, the cognitive process of the brain is decoded by a model following reverse engineering. Decoding the EEG signals induced by the facial emotion images, the common channel in the visual domain can approach the cognitive process in the cognitive domain. Moreover, the knowledge specific to each domain is found in each private channel using an adversarial strategy. After learning, without the participation of the EEG signals, only the concatenation of both channels in the visual domain is used to classify facial emotion images based on the visual knowledge of the machine and the cognitive knowledge learned from the brain. Experiments demonstrate that the proposed method can produce excellent performance on several public datasets. Further experiments show that the proposed method trained from the EEG signals has good generalization ability on new datasets and can be applied to other network models, illustrating the potential for practical applications.}, }
@article {pmid37028310, year = {2023}, author = {Hsu, WY and Cheng, YW}, title = {EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {1659-1669}, doi = {10.1109/TNSRE.2023.3255233}, pmid = {37028310}, issn = {1558-0210}, abstract = {In brain-computer interface (BCI) work, how correctly identifying various features and their corresponding actions from complex Electroencephalography (EEG) signals is a challenging technology. However, most current methods do not consider EEG feature information in spatial, temporal and spectral domains, and the structure of these models cannot effectively extract discriminative features, resulting in limited classification performance. To address this issue, we propose a novel text motor-imagery EEG discrimination method, namely wavelet-based temporal-spectral-attention correlation coefficient (WTS-CC), to simultaneously consider the features and their weighting in spatial, EEG-channel, temporal and spectral domains in this study. The initial Temporal Feature Extraction (iTFE) module extracts the initial important temporal features of MI EEG signals. The Deep EEG-Channel-attention (DEC) module is then proposed to automatically adjust the weight of each EEG channel according to its importance, thereby effectively enhancing more important EEG channels and suppressing less important EEG channels. Next, the Wavelet-based Temporal-Spectral-attention (WTS) module is proposed to obtain more significant discriminative features between different MI tasks by weighting features on two-dimensional time-frequency maps. Finally, a simple discrimination module is used for MI EEG discrimination. The experimental results indicate that the proposed text WTS-CC method can achieve promising discrimination performance that outperforms the state-of-the-art methods in terms of classification accuracy, Kappa coefficient, F1 score, and AUC on three public datasets.}, }
@article {pmid37028281, year = {2023}, author = {Hu, Y and Liu, Y and Zhang, S and Zhang, T and Dai, B and Peng, B and Yang, H and Dai, Y}, title = {A Cross-Space CNN With Customized Characteristics for Motor Imagery EEG Classification.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {1554-1565}, doi = {10.1109/TNSRE.2023.3249831}, pmid = {37028281}, issn = {1558-0210}, abstract = {The classification of motor imagery-electroencephalogram(MI-EEG)based brain-computer interface(BCI)can be used to decode neurological activities, which has been widely applied in the control of external devices. However, two factors still hinder the improvement of classification accuracy and robustness, especially in multi-class tasks. First, existing algorithms are based on a single space (measuring or source space). They suffer from the holistic low spatial resolution of the measuring space or the locally high spatial resolution information accessed from the source space, failing to provide holistic and high-resolution representations. Second, the subject specificity is not sufficiently characterized, resulting in the loss of personalized intrinsic information. Therefore, we propose a cross-space convolutional neural network (CS-CNN) with customized characteristics for four-class MI-EEG classification. This algorithm uses the modified customized band common spatial patterns (CBCSP) and duplex mean-shift clustering (DMSClustering) to express the specific rhythms and source distribution information in cross-space. At the same time, multi-view features from the time, frequency and space domains are extracted, connecting with CNN to fuse the characteristics from two spaces and classify them. MI-EEG was collected from 20 subjects. Lastly, the classification accuracy of the proposed is 96.05% with real MRI information and 94.79% without MRI in the private dataset. And the results in the BCI competition IV-2a show that CS-CNN outperforms the state-of-the-art algorithms, achieving an accuracy improvement of 1.98%, and a standard deviation reduction of 5.15%.}, }
@article {pmid37028070, year = {2023}, author = {Zhang, Y and Xie, SQ and Shi, C and Li, J and Zhang, ZQ}, title = {Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3250953}, pmid = {37028070}, issn = {1558-0210}, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an inter-subject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.}, }
@article {pmid37028028, year = {2023}, author = {Lou, H and Ye, Z and Yao, L and Zhang, Y}, title = {Less is More: Brain Functional Connectivity Empowered Generalisable Intention Classification with Task-relevant Channel Selection.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3252610}, pmid = {37028028}, issn = {1558-0210}, abstract = {Electroencephalography (EEG) signals are gaining popularity in Brain-Computer Interface (BCI)-based rehabilitation and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account. Specifically, we construct a task-adaptive graph representation of the brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. We empirically show that the proposed approach outperforms the state-of-the-art, with around 1% and 11% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data, suggesting a possible shift in direction for future works other than simply scaling up the model.}, }
@article {pmid37028015, year = {2023}, author = {Hu, Y and Liu, Y and Zhang, S and Zhang, T and Dai, B and Peng, B and Yang, H and Dai, Y}, title = {A Cross-Space CNN with Customized Characteristics for Motor Imagery EEG Classification (September 2022).}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3249831}, pmid = {37028015}, issn = {1558-0210}, abstract = {The classification of motor imagery-electroen-cephalogram(MI-EEG)based brain-computer interface(BCI) can be used to decode neurological activities, which has been widely applied in the control of external devices. However, two factors still hinder the improvement of classification accuracy and robustness, especially in multi-class tasks. First, existing algorithms are based on a single space (measuring or source space). They suffer from the holistic low spatial resolution of the measuring space or the locally high spatial resolution information accessed from the source space, failing to provide holistic and high-resolution representations. Second, the subject specificity is not sufficiently characterized, resulting in the loss of personalized intrinsic information. Therefore, we propose a cross-space convolutional neural network (CS-CNN) with customized characteristics for four-class MI-EEG classification. This algorithm uses the modified customized band common spatial patterns (CBCSP) and duplex mean-shift clustering (DMSClustering) to express the specific rhythms and source distribution information in cross-space. At the same time, multi-view features from the time, frequency and space domains are extracted, connecting with CNN to fuse the characteristics from two spaces and classify them. MI-EEG was collected from 20 subjects. Lastly, the classification accuracy of the proposed is 96.05% with real MRI information and 94.79% without MRI in the private dataset. And the results in the BCI competition Ⅳ-2a show that CS-CNN outperforms the state-of-the-art algorithms, achieving an accuracy improvement of 1.98%, and a standard deviation reduction of 5.15%.}, }
@article {pmid37027669, year = {2023}, author = {Gong, P and Wang, P and Zhou, Y and Zhang, D}, title = {A Spiking Neural Network with Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3246989}, pmid = {37027669}, issn = {1558-0210}, abstract = {Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. However, most existing methods do not effectively mine the specific spatial topology of EEG channels and temporal dependencies of the encoded EEG spikes. Moreover, most are designed for specific BCI tasks and lack some generality. Hence, this study presents a novel SNN model with the customized spike-based adaptive graph convolution and long short-term memory (LSTM), termed SGLNet, for EEG-based BCIs. Specifically, we first adopt a learnable spike encoder to convert the raw EEG signals into spike trains. Then, we tailor the concepts of the multi-head adaptive graph convolution to SNN so that it can make good use of the intrinsic spatial topology information among distinct EEG channels. Finally, we design the spike-based LSTM units to further capture the temporal dependencies of the spikes. We evaluate our proposed model on two publicly available datasets from two representative fields of BCI, notably emotion recognition, and motor imagery decoding. The empirical evaluations demonstrate that SGLNet consistently outperforms existing state-of-the-art EEG classification algorithms. This work provides a new perspective for exploring high-performance SNNs for future BCIs with rich spatiotemporal dynamics.}, }
@article {pmid37027653, year = {2023}, author = {Wang, H and Chen, P and Zhang, M and Zhang, J and Sun, X and Li, M and Yang, X and Gao, Z}, title = {EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3243339}, pmid = {37027653}, issn = {2162-2388}, abstract = {A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.}, }
@article {pmid37027633, year = {2023}, author = {Fan, Y and Mao, H and Li, Q}, title = {A Model-Agnostic Feature Attribution Approach to Magnetoencephalography Predictions Based on Shapley Value.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3248139}, pmid = {37027633}, issn = {2168-2208}, abstract = {Deep learning has greatly enhanced the predictive performance of magnetoencephalography (MEG) decoding. However, the lack of interpretability has become a major obstacle to the practical application of deep learning-based MEG decoding algorithms, which may lead to non-compliance with legal requirements and distrust among end-users. To address this issue, this paper proposes a feature attribution approach, which can provide interpretative support for each individual MEG prediction for the first time. The approach first transforms a MEG sample into a feature set, then assigns contribution weights to each feature using modified Shapley values, which are optimized by filtering reference samples and generating antithetic sample pairs. Experimental results show that the Area Under the Deletion test Curve (AUDC) of the approach is as low as 0.005, which means a better attribution accuracy compared to typical computer vision algorithms. Visualization analysis reveals that the key features of the model decisions are consistent with neurophysiological theories. Based on these key features, the input signal can be compressed to one-sixteenth of its original size with only a 0.19% loss in classification performance. Another benefit of our approach is that it is model-agnostic, enabling its utilization for various decoding models and brain-computer interface (BCI) applications.}, }
@article {pmid35104233, year = {2023}, author = {Ganaie, MA and Tanveer, M and Beheshti, I}, title = {Brain Age Prediction With Improved Least Squares Twin SVR.}, journal = {IEEE journal of biomedical and health informatics}, volume = {27}, number = {4}, pages = {1661-1669}, doi = {10.1109/JBHI.2022.3147524}, pmid = {35104233}, issn = {2168-2208}, abstract = {Alzheimer's disease (AD) is the prevalent form of dementia and shares many aspects with the aging pattern of the abnormal brain. Machine learning models like support vector regression (SVR) based models have been successfully employed in the estimation of brain age. However, SVR is computationally inefficient than twin support vector machine based models. Hence, different twin support vector machine based models like twin SVR (TSVR), ε-TSVR, and Lagrangian TSVR (LTSVR) models have been used for the regression problems. ε-TSVR and LTSVR models seek a pair of ε-insensitive proximal planes for generation of end regressor. However, SVR and TSVR based models have several drawbacks- i) SVR model is computationally inefficient compared to the TSVR based models. ii) Twin SVM based models involve the computation of matrix inverse which is intractable in real world scenario's. iii) Both TSVR and LTSVR models are based on empirical risk minimization principle and hence may be prone to overfitting. iv) TSVR and LTSVR assume that the matrices appearing in their formulation are positive definite which may not be satisfied in real world scenario's. To overcome these issues, we formulate improved least squares twin support vector regression (ILSTSVR). The proposed ILSTSVR modifies the TSVR by replacing the inequality constraints with the equality constraints and minimizes the slack variables using squares of L2 norm instead of L1. Also, we introduce a different Lagrangian function to avoid the computation of matrix inverses. We evaluated the proposed ILSTSVR model on the subjects including cognitively healthy, mild cognitive impairment and Alzheimer's disease for brain-age estimation. Experimental evaluation and statistical tests demonstrate the efficiency of the proposed ILSTSVR model for brain-age prediction.}, }
@article {pmid37027569, year = {2023}, author = {Kalra, J and Mittal, P and Mittal, N and Arora, A and Tewari, U and Chharia, A and Upadhyay, R and Kumar, V and Longo, L}, title = {How Visual Stimuli Evoked P300 is Transforming the Brain-Computer Interface Landscape: A PRISMA Compliant Systematic Review.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {1429-1439}, doi = {10.1109/TNSRE.2023.3246588}, pmid = {37027569}, issn = {1558-0210}, abstract = {Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants' age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning.}, }
@article {pmid37027558, year = {2023}, author = {Ke, Y and Du, J and Liu, S and Ming, D}, title = {Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3246359}, pmid = {37027558}, issn = {1558-0210}, abstract = {This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency-unified (FU) framework that conducted control state detection using a unified classifier trained on features related to all candidate frequencies was proposed to compare with the FS framework. Offline evaluation using data lengths within 1 s found that the FS framework achieved excellent performance and significantly outperformed the FU framework. 14-target FS and FU asynchronous systems were separately constructed by incorporating a simple dynamic stopping strategy and validated using a cue-guided selection task in an online experiment. Using averaged data length of 591.63±5.65 ms, the online FS system significantly outperformed the FU system and achieved an information transfer rate, true positive rate, false positive rate, and balanced accuracy of 124.95±12.35 bits/min, 93.16±4.4%, 5.21±5.85%, and 92.89±4.02%, respectively. The FS system was also of higher reliability by accepting more correctly identified SSVEP trials and rejecting more wrongly identified ones. These results suggest that the FS framework has great potential to enhance the control state detection for high-speed asynchronous SSVEP-BCIs.}, }
@article {pmid37027528, year = {2023}, author = {Shi, N and Li, X and Liu, B and Yang, C and Wang, Y and Gao, X}, title = {Representative-based Cold Start for Adaptive SSVEP-BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3245654}, pmid = {37027528}, issn = {1558-0210}, abstract = {OBJECTIVE: The tradeoff between calibration effort and model performance still hinders the user experience for steady-state visual evoked brain-computer interfaces (SSVEP-BCI). To address this issue and improve model generalizability, this work investigated the adaptation from the cross-dataset model to avoid the training process, while maintaining high prediction ability.<br><br>METHODS: When a new subject enrolls, a group of user-independent (UI) models is recommended as the representative from a multi-source data pool. The representative model is then augmented with online adaptation and transfer learning techniques based on user-dependent (UD) data. The proposed method is validated on both offline (N=55) and online (N=12) experiments.<br><br>RESULTS: Compared with the UD adaptation, the recommended representative model relieved approximately 160 trials of calibration efforts for a new user. In the online experiment, the time window decreased from 2 s to 0.56&#xb1;0.2 s, while maintaining high prediction accuracy of 0.89-0.96. Finally, the proposed method achieved the average information transfer rate (ITR) of 243.49 bits/min, which is the highest ITR ever reported in a complete calibration-free setting. The results of the offline result were consistent with the online experiment.<br><br>CONCLUSION: Representatives can be recommended even in a cross-subject/device/session situation. With the help of represented UI data, the proposed method can achieve sustained high performance without a training process.<br><br>SIGNIFICANCE: This work provides an adaptive approach to the transferable model for SSVEP-BCIs, enabling a more generalized, plug-and-play and high-performance BCI free of calibrations.}, }
@article {pmid37027527, year = {2023}, author = {Wang, J and Bi, L and Feleke, AG and Fei, W}, title = {MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3245617}, pmid = {37027527}, issn = {1558-0210}, abstract = {Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients' residual or intact movement functions, is a more intuitive and natural paradigm. Based on the ME paradigm, we can decode voluntary hand movement intentions from electroencephalography (EEG) signals. Numerous studies have investigated EEG-based unimanual movement decoding. Moreover, some studies have explored bimanual movement decoding since bimanual coordination is important in daily-life assistance and bilateral neurorehabilitation therapy. However, the multi-class classification of the unimanual and bimanual movements shows weak performance. To address this problem, in this work, we propose a neurophysiological signatures-driven deep learning model utilizing the movement-related cortical potentials (MRCPs) and event-related synchronization/ desynchronization (ERS/D) oscillations for the first time, inspired by the finding that brain signals encode motor-related information with both evoked potentials and oscillation components in ME. The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. Results show that our proposed model has superior performance to the baseline methods. Six-class classification accuracies of unimanual and bimanual movements achieved 80.3%. Besides, each feature module of our model contributes to the performance. This work is the first to fuse the MRCPs and ERS/D oscillations of ME in deep learning to enhance the multi-class unimanual and bimanual movements' decoding performance. This work can facilitate the neural decoding of unimanual and bimanual movements for neurorehabilitation and assistance.}, }
@article {pmid37027253, year = {2023}, author = {Wang, J and Yao, L and Wang, Y}, title = {IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {1900-1911}, doi = {10.1109/TNSRE.2023.3257319}, pmid = {37027253}, issn = {1558-0210}, abstract = {OBJECTIVE: The key principle of motor imagery (MI) decoding for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from spectral, spatial, and temporal domains jointly and efficiently, whereas limited, noisy, and non-stationary EEG samples challenge the advanced design of decoding algorithms.<br><br>METHODS: Inspired by the concept of cross-frequency coupling and its correlation with different behavioral tasks, this paper proposes a lightweight Interactive Frequency Convolutional Neural Network (IFNet) to explore cross-frequency interactions for enhancing representation of MI characteristics. IFNet first extracts spectro-spatial features in low and high-frequency bands, respectively. Then the interplay between the two bands is learned using an element-wise addition operation followed by temporal average pooling. Combined with repeated trial augmentation as a regularizer, IFNet yields spectro-spatio-temporally robust features for the final MI classification. We conduct extensive experiments on two benchmark datasets: the BCI competition IV 2a (BCIC-IV-2a) dataset and the OpenBMI dataset.<br><br>RESULTS: Compared with state-of-the-art MI decoding algorithms, IFNet achieves significantly superior classification performance on both datasets while improving the winner's result in BCIC-IV-2a by 11%. Moreover, by conducting sensitivity analysis on decision windows, we show IFNet attains the best trade-off between decoding speed and accuracy. Detailed analysis and visualization verify IFNet can capture the coupling across frequency bands along with the known MI signatures.<br><br>CONCLUSION: We demonstrate the effectiveness and superiority of the proposed IFNet for MI decoding.<br><br>SIGNIFICANCE: This study suggests IFNet holds promise for rapid response and accurate control in MI-BCI applications.}, }
@article {pmid37025702, year = {2023}, author = {Xiang, L and Harel, A and Todorova, R and Gao, H and Sara, SJ and Wiener, SI}, title = {Locus coeruleus noradrenergic neurons phase-lock to prefrontal and hippocampal infra-slow rhythms that synchronize to behavioral events.}, journal = {Frontiers in cellular neuroscience}, volume = {17}, number = {}, pages = {1131151}, pmid = {37025702}, issn = {1662-5102}, abstract = {The locus coeruleus (LC) is the primary source of noradrenergic projections to the forebrain, and, in prefrontal cortex, is implicated in decision-making and executive function. LC neurons phase-lock to cortical infra-slow wave oscillations during sleep. Such infra-slow rhythms are rarely reported in awake states, despite their interest, since they correspond to the time scale of behavior. Thus, we investigated LC neuronal synchrony with infra-slow rhythms in awake rats performing an attentional set-shifting task. Local field potential (LFP) oscillation cycles in prefrontal cortex and hippocampus on the order of 0.4 Hz phase-locked to task events at crucial maze locations. Indeed, successive cycles of the infra-slow rhythms showed different wavelengths, as if they are periodic oscillations that can reset phase relative to salient events. Simultaneously recorded infra-slow rhythms in prefrontal cortex and hippocampus could show different cycle durations as well, suggesting independent control. Most LC neurons (including optogenetically identified noradrenergic neurons) recorded here were phase-locked to these infra-slow rhythms, as were hippocampal and prefrontal units recorded on the LFP probes. The infra-slow oscillations also phase-modulated gamma amplitude, linking these rhythms at the time scale of behavior to those coordinating neuronal synchrony. This would provide a potential mechanism where noradrenaline, released by LC neurons in concert with the infra-slow rhythm, would facilitate synchronization or reset of these brain networks, underlying behavioral adaptation.}, }
@article {pmid37023989, year = {2023}, author = {Michalke, L and Dreyer, AM and Borst, JP and Rieger, JW}, title = {Inter-individual single-trial classification of MEG data using M-CCA.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120079}, doi = {10.1016/j.neuroimage.2023.120079}, pmid = {37023989}, issn = {1095-9572}, abstract = {Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naive solution is to assume that participants' recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to anatomical and functional differences between individual brains. In magnetoencephalography (MEG) recordings the problem of inter-subject alignment is exacerbated by the susceptibility of MEG to individual cortical folding patterns as well as the inter-subject variability of sensor locations over the brain due to the use of a fixed helmet. Hence, an approach to combine MEG data over individual brains should relax the assumptions that a) brain anatomy and function are tightly linked and b) that the same sensors capture functionally comparable brain activation across individuals. Here we use multiset canonical correlation analysis (M-CCA) to find a common representation of MEG activations recorded from 15 participants performing a grasping task. The M-CCA algorithm was applied to transform the data of a set of multiple participants into a common space with maximum correlation between participants. Importantly, we derive a method to transform data from a new, previously unseen participant into this common representation. This makes it useful for applications that require transfer of models derived from a group of individuals to new individuals. We demonstrate the usefulness and superiority of the approach with respect to previously used approaches. Finally, we show that our approach requires only a small number of labeled data from the new participant. The proposed method demonstrates that functionally motivated common spaces have potential applications in reducing training time of online brain-computer interfaces, where models can be pre-trained on previous participants/sessions. Moreover, inter-subject alignment via M-CCA has the potential for combining data of different participants and could become helpful in future endeavors on large open datasets.}, }
@article {pmid37023639, year = {2023}, author = {Peng, B and Zhang, Y and Wang, M and Chen, J and Gao, D}, title = {T-A-MFFNet: Multi-feature fusion network for EEG analysis and driving fatigue detection based on time domain network and attention network.}, journal = {Computational biology and chemistry}, volume = {104}, number = {}, pages = {107863}, doi = {10.1016/j.compbiolchem.2023.107863}, pmid = {37023639}, issn = {1476-928X}, abstract = {Driving fatigue detection based on EEG signals is a research hotspot in applying brain-computer interfaces. EEG signal is complex, unstable, and nonlinear. Most existing methods rarely analyze the data characteristics from multiple dimensions, so it takes work to analyze the data comprehensively. To analyze EEG signals more comprehensively, this paper evaluates a feature extraction strategy of EEG data based on differential entropy (DE). This method combines the characteristics of different frequency bands, extracts the frequency domain characteristics of EEG, and retains the spatial information between channels. This paper proposes a multi-feature fusion network (T-A-MFFNet) based on the time domain and attention network. The model is composed of a time domain network (TNet), channel attention network (CANet), spatial attention network (SANet), and multi-feature fusion network(MFFNet) based on a squeeze network. T-A-MFFNet aims to learn more valuable features from the input data to achieve good classification results. Specifically, the TNet network extracts high-level time series information from EEG data. CANet and SANet are used to fuse channel and spatial features. They use MFFNet to merge multi-dimensional features and realize classification. The validity of the model is verified on the SEED-VIG dataset. The experimental results show that the accuracy of the proposed method reaches 85.65 %, which is superior to the current popular model. The proposed method can learn more valuable information from EEG signals to improve the ability to identify fatigue status and promote the development of the research field of driving fatigue detection based on EEG signals.}, }
@article {pmid37023540, year = {2023}, author = {Gao, Y and Zhang, C and Fang, F and Cammon, J and Zhang, Y}, title = {Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition.}, journal = {Computers in biology and medicine}, volume = {158}, number = {}, pages = {106887}, doi = {10.1016/j.compbiomed.2023.106887}, pmid = {37023540}, issn = {1879-0534}, abstract = {Tensor analysis can comprehensively retain multidomain characteristics, which has been employed in EEG studies. However, existing EEG tensor has large dimension, making it difficult to extract features. Traditional Tucker decomposition and Canonical Polyadic decomposition(CP) decomposition algorithms have problems of low computational efficiency and weak capability to extract features. To solve the above problems, Tensor-Train(TT) decomposition is adopted to analyze the EEG tensor. Meanwhile, sparse regularization term can then be added to TT decomposition, resulting in a sparse regular TT decomposition (SR-TT). The SR-TT algorithm is proposed in this paper, which has higher accuracy and stronger generalization ability than state-of-the-art decomposition methods. The SR-TT algorithm was verified with BCI competition III and BCI competition IV dataset and achieved 86.38% and 85.36% classification accuracies, respectively. Meanwhile, compared with traditional tensor decomposition (Tucker and CP) method, the computational efficiency of the proposed algorithm was improved by 16.49 and 31.08 times in BCI competition III and 20.72 and 29.45 times more efficient in BCI competition IV. Besides, the method can leverage tensor decomposition to extract spatial features, and the analysis is performed by pairs of brain topography visualizations to show the changes of active brain regions under the task condition. In conclusion, the proposed SR-TT algorithm in the paper provides a novel insight for tensor EEG analysis.}, }
@article {pmid37023162, year = {2023}, author = {Barmpas, K and Panagakis, Y and Bakas, S and Adamos, DA and Laskaris, N and Zafeiriou, S}, title = {Improving Generalization of CNN-based Motor-Imagery EEG Decoders via Dynamic Convolutions.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3265304}, pmid = {37023162}, issn = {1558-0210}, abstract = {Deep Convolutional Neural Networks (CNNs) have recently demonstrated impressive results in electroencephalogram (EEG) decoding for several Brain-Computer Interface (BCI) paradigms, including Motor-Imagery (MI). However, neurophysiological processes underpinning EEG signals vary across subjects causing covariate shifts in data distributions and hence hindering the generalization of deep models across subjects. In this paper, we aim to address the challenge of inter-subject variability in MI. To this end, we employ causal reasoning to characterize all possible distribution shifts in the MI task and propose a dynamic convolution framework to account for shifts caused by the inter-subject variability. Using publicly available MI datasets, we demonstrate improved generalization performance (up to 5%) across subjects in various MI tasks for four well-established deep architectures.}, }
@article {pmid37022899, year = {2023}, author = {Chen, X and Liu, B and Wang, Y and Cui, H and Dong, J and Ma, R and Li, N and Gao, X}, title = {Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3243786}, pmid = {37022899}, issn = {1558-0210}, abstract = {The brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have been extensively explored due to their advantages in terms of high communication speed and smaller calibration time. The visual stimuli in the low- and medium-frequency ranges are adopted in most of the existing studies for eliciting SSVEPs. However, there is a need to further improve the comfort of these systems. The high-frequency visual stimuli have been used to build BCI systems and are generally considered to significantly improve the visual comfort, but their performance is relatively low. The distinguishability of 16-class SSVEPs encoded by the three frequency ranges, i.e., 31-34.75 Hz with an interval of 0.25 Hz, 31-38.5 Hz with an interval of 0.5 Hz, 31-46 Hz with an interval of 1 Hz, is explored in this study. We compare classification accuracy and information transfer rate (ITR) of the corresponding BCI system. According to the optimized frequency range, this study builds an online 16-target high frequency SSVEP-BCI and verifies the feasibility of the proposed system based on 21 healthy subjects. The BCI based on visual stimuli with the narrowest frequency range, i.e., 31-34.5 Hz, have the highest ITR. Therefore, the narrowest frequency range is adopted to build an online BCI system. An averaged ITR obtained from the online experiment is 153.79 ± 6.39 bits/min. These findings contribute to the development of more efficient and comfortable SSVEP-based BCIs.}, }
@article {pmid35157588, year = {2023}, author = {Shaeri, M and Sodagar, AM}, title = {Data Transformation in the Processing of Neuronal Signals: A Powerful Tool to Illuminate Informative Contents.}, journal = {IEEE reviews in biomedical engineering}, volume = {16}, number = {}, pages = {611-626}, doi = {10.1109/RBME.2022.3151340}, pmid = {35157588}, issn = {1941-1189}, mesh = {Humans ; Algorithms ; Signal Processing, Computer-Assisted ; *Data Compression ; *Brain-Computer Interfaces ; Neurophysiology ; }, abstract = {Neuroscientists seek efficient solutions for deciphering the sophisticated unknowns of the brain. Effective development of complicated brain-related tools is the focal point of research in neuroscience and neurotechnology. Thanks to today's technological advancements, the physical development of high-density and high-resolution neural interfaces has been made possible. This is where the critical bottleneck in receiving the expected functionality from such devices shifts to transferring, processing, and subsequently analyzing the massive neurophysiological extra-cellular data recorded. To respond to this inevitable concern, a spectrum of neuronal signal processing techniques have been proposed to extract task-related informative content of the signals conveying neuronal activities, and eliminate the irrelevant contents. Such techniques provide powerful tools for a wide range of neuroscience research, from low-level perception to high-level cognition. Data transformations are among the most efficient processing techniques that serve this purpose by properly changing the data representation. Mapping the data from its original domain (i.e., the time-space domain) to a new representational domain, data transformations change the viewing angle of observing the informative content of the data. This paper reviews the employment of data transformations in order to process neuronal signals and their three key applications, including spike detection, spike sorting, and data compression.}, }
@article {pmid33531303, year = {2023}, author = {Akan, OB and Ramezani, H and Civas, M and Cetinkaya, O and Bilgin, BA and Abbasi, NA}, title = {Information and Communication Theoretical Understanding and Treatment of Spinal Cord Injuries: State-of-The-Art and Research Challenges.}, journal = {IEEE reviews in biomedical engineering}, volume = {16}, number = {}, pages = {332-347}, doi = {10.1109/RBME.2021.3056455}, pmid = {33531303}, issn = {1941-1189}, mesh = {Humans ; *Spinal Cord Injuries/therapy ; Brain ; Technology ; *Brain-Computer Interfaces ; }, abstract = {Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio-Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research.}, }
@article {pmid37022898, year = {2023}, author = {Jia, H and Yu, S and Yin, S and Liu, L and Yi, C and Xue, K and Li, F and Yao, D and Xu, P and Zhang, T}, title = {A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM For MI-BCI Classification.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3243992}, pmid = {37022898}, issn = {1558-0210}, abstract = {Accurately decoding motor imagery (MI) brain-computer interface (BCI) tasks has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less subject information and low signal-to-noise ratio of MI electroencephalography (EEG) signals make it difficult to decode the movement intentions of users. In this study, we proposed an end-to-end deep learning model, a multi-branch spectral-temporal convolutional neural network with channel attention and LightGBM model (MBSTCNN-ECA-LightGBM), to decode MI-EEG tasks. We first constructed a multi branch CNN module to learn spectral-temporal domain features. Subsequently, we added an efficient channel attention mechanism module to obtain more discriminative features. Finally, LightGBM was applied to decode the MI multi-classification tasks. The within-subject cross-session training strategy was used to validate classification results. The experimental results showed that the model achieved an average accuracy of 86% on the two-class MI-BCI data and an average accuracy of 74% on the four-class MI-BCI data, which outperformed current state-of-the-art methods. The proposed MBSTCNN-ECA-LightGBM can efficiently decode the spectral and temporal domain information of EEG, improving the performance of MI-based BCIs.}, }
@article {pmid37022880, year = {2023}, author = {Ge, S and Yang, H and Wang, R and Leng, Y and Iramina, K and Lin, P and Wang, H}, title = {Block Distributed Joint Temporal-Frequency-Phase Modulation for Steady-State Visual Evoked Potential Based Brain-Computer Interface With a Limited Number of Frequencies.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3244277}, pmid = {37022880}, issn = {2168-2208}, abstract = {How to encode as many targets as possible with limited frequency resources is a grave problem that restricts the application of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In the current study, we propose a novel block-distributed joint temporal-frequency-phase modulation method for a virtual speller based on SSVEP-based BCI. A 48-target speller keyboard array is virtually divided into eight blocks and each block contains six targets. The coding cycle consists of two sessions: in the first session, each block flashes at different frequencies while all the targets in the same block flicker at the same frequency; in the second session, all the targets in the same block flash at different frequencies. Using this method, 48 targets can be coded with only eight frequencies, which greatly reduces the frequency resources required, and average accuracies of 86.81 ± 9.41% and 91.36 ± 6.41% were obtained for both the offline and online experiments. This study provides a new coding approach for a large number of targets with a small number of frequencies, which can further expand the application potential of SSVEP-based BCI.}, }
@article {pmid37022873, year = {2023}, author = {Zhang, S and Gao, X and Cui, H and Chen, X}, title = {Transcranial Direct Current Stimulation-based Neuromodulation Improves the Performance of Brain-Computer Interfaces Based on Steady-State Visual Evoked Potential.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3245079}, pmid = {37022873}, issn = {1558-0210}, abstract = {The study of brain state estimation and intervention methods is of great significance for the utility of brain-computer interfaces (BCIs). In this paper, a neuromodulation technology using transcranial direct current stimulation (tDCS) is explored to improve the performance of steady-state visual evoked potential (SSVEP)-based BCIs. The effects of pre-stimulation, sham-tDCS and anodal-tDCS are analyzed through a comparison of the EEG oscillations and fractal component characteristics. In addition, in this study, a novel brain state estimation method is introduced to assess neuromodulation-induced changes in brain arousal for SSVEP-BCIs. The results suggest that tDCS, and anodal-tDCS in particular, can be used to increase SSVEP amplitude and further improve the performance of SSVEP-BCIs. Furthermore, evidence from fractal features further validates that tDCS-based neuromodulation induces an increased level of brain state arousal. The findings of this study provide insights into the improvement of BCI performance based on personal state interventions and provide an objective method for quantitative brain state monitoring that may be used for EEG modeling of SSVEP-BCIs.}, }
@article {pmid37022842, year = {2023}, author = {Wei, F and Xu, X and Jia, T and Zhang, D and Wu, X}, title = {A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3243257}, pmid = {37022842}, issn = {1558-0210}, abstract = {Individual differences among different subjects pose a great challenge to motor imagery (MI) decoding. Multi-source transfer learning (MSTL) is one of the most promising ways to reduce individual differences, which can utilize rich information and align the data distribution among different subjects. However, most MSTL methods in MI-BCI combine all data in the source subjects into a single mixed domain, which will ignore the effect of important samples and the large differences in multiple source subjects. To address these issues, we introduce transfer joint matching and improve it to multi-source transfer joint matching (MSTJM) and weighted MSTJM (wMSTJM). Different from previous MSTL methods in MI, our methods align the data distribution for each pair of subjects, and then integrate the results by decision fusion. Besides that, we design an inter-subject MI decoding framework to verify the effectiveness of these two MSTL algorithms. It mainly consists of three modules: covariance matrix centroid alignment in the Riemannian space, source selection in the Euclidean space after tangent space mapping to reduce negative transfer and computation overhead, and further distribution alignment by MSTJM or wMSTJM. The superiority of this framework is verified on two common public MI datasets from BCI competition IV. The average classification accuracy of the MSTJM and wMSTJ methods outperformed other state-of-the-art methods by at least 4.24% and 2.62% respectively. It's promising to advance the practical applications of MI-BCI.}, }
@article {pmid37022841, year = {2023}, author = {Nakanishi, M and Miner, A and Jung, TP and Graves, J}, title = {Novel Moving Steady-State Visual Evoked Potential Stimulus to Assess Afferent and Efferent Dysfunction in Multiple Sclerosis.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3243554}, pmid = {37022841}, issn = {1558-0210}, abstract = {Afferent and efferent visual dysfunction are prominent features of multiple sclerosis (MS). Visual outcomes have been shown to be robust biomarkers of the overall disease state. Unfortunately, precise measurement of afferent and efferent function is typically limited to tertiary care facilities, which have the equipment and analytical capacity to make these measurements, and even then, only a few centers can accurately quantify both afferent and efferent dysfunction. These measurements are currently unavailable in acute care facilities (ER, hospital floors). We aimed to develop a moving multifocal steady-state visual evoked potential (mfSSVEP) stimulus to simultaneously assess afferent and efferent dysfunction in MS for application on a mobile platform. The brain-computer interface (BCI) platform consists of a head-mounted virtual-reality headset with electroencephalogram (EEG) and electrooculogram (EOG) sensors. To evaluate the platform, we recruited consecutive patients who met the 2017 MS McDonald diagnostic criteria and healthy controls for a pilot cross-sectional study. Nine MS patients (mean age 32.7 years, SD 4.33) and ten healthy controls (24.9 years, SD 7.2) completed the research protocol. The afferent measures based on mfSSVEPs showed a significant difference between the groups (signal-to-noise ratio of mfSSVEPs for controls: 2.50 ± 0.72 vs. MS: 2.04 ± 0.47) after controlling for age (p = 0.049). In addition, the moving stimulus successfully induced smooth pursuit movement that can be measured by the EOG signals. There was a trend for worse smooth pursuit tracking in cases vs. controls, but this did not reach nominal statistical significance in this small pilot sample. This study introduces a novel moving mfSSVEP stimulus for a BCI platform to evaluate neurologic visual function. The moving stimulus showed a reliable capability to assess both afferent and efferent visual functions simultaneously.}, }
@article {pmid37022824, year = {2023}, author = {Wong, CM and Wang, Z and Wang, B and Rosa, A and Jung, TP and Wan, F}, title = {Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3243290}, pmid = {37022824}, issn = {1558-0210}, abstract = {OBJECTIVE: Multi-frequency-modulated visual stimulation scheme has been shown effective for the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) recently, especially in increasing the visual target number with less stimulus frequencies and mitigating the visual fatigue. However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance.<br><br>APPROACH: To improve the recognition performance, this study proposes a phase difference constrained CCA (pdCCA), which assumes that the multi-frequency-modulated SSVEPs share a common spatial filter over different frequencies and have a specified phase difference. Specifically, during the CCA computation, the phase differences of the spatially filtered SSVEPs are constrained using the temporal concatenation of the sine-cosine reference signals with the pre-defined initial phases.<br><br>MAIN RESULTS: We evaluate the performance of the proposed pdCCA-based method on three representative multi-frequency-modulated visual stimulation paradigms (i.e., based on the multi-frequency sequential coding, the dual-frequency, and the amplitude modulation). The evaluation results on four SSVEP datasets (Dataset Ia, Ib, II, and III) show that the pdCCA-based method can significantly outperform the current CCA method in terms of recognition accuracy. It improves the accuracy by 22.09% in Dataset Ia, 20.86% in Dataset Ib, 8.61% in Dataset II, and 25.85% in Dataset III.<br><br>SIGNIFICANCE: The pdCCA-based method, which actively controls the phase difference of the multi-frequency-modulated SSVEPs after spatial filtering, is a new calibration-free method for multi-frequency-modulated SSVEP-based BCIs.}, }
@article {pmid37022818, year = {2023}, author = {Mammone, N and Ieracitano, C and Adeli, H and Morabito, FC}, title = {AutoEncoder Filter Bank Common Spatial Patterns to decode Motor Imagery from EEG.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3243698}, pmid = {37022818}, issn = {2168-2208}, abstract = {The present paper introduces a novel method to decode imagined movement from electroencephalographic (EEG) signals. Decoding the imagined movement with good accuracy is a challenging topic in motor imagery (MI) BCIs, poor accuracy may indeed hinder the application of such systems in practice. The present paper introduces an extension of the well-established Filter Bank Common Spatial Patterns (FBCSP) algorithm, named AutoEncoder(AE)-FBCSP, to benefit from the ability of AE to learn how to map data from the feature space onto a latent space where information relevant for classification are is embedded. The proposed method is based on a global (cross-subject) and subsequent transfer learning subject-specific (intra-subject) approach. A multi-way extension of AE-FBCSP is also introduced in this paper. The proposed methodology consists of recording high-density EEG (64 electrodes). Features are extracted by means of FBCSP and used to train a custom AE, in an unsupervised way, to project the features into a compressed latent space. Latent features then are used to train a supervised classifier (feed forward neural network) to decode the imagined movement. The algorithm was tested using a dataset of EEG extracted from a publicly available database of data collected from 109 subjects. AE-FBCSP was extensively tested in the 3-way (right-hand vs left-hand motor imagery vs resting) classification and also in the 2-way, 4-way and 5-way ones, both in cross- and intra-subject analysis. AE-FBCSP outperformed standard FBCSP in a statistically significant way (p 0.05) and outperformed also comparable methods in the literature applied to the same dataset. AE-FBCSP achieved an average accuracy of 89.09% in the 3-way subject-specific classification. With AE-FBCSP, 71.43% of subjects achieved a very high accuracy (87.68%) whereas no subject achieved an accuracy 87.68% with FBCSP. One of the most interesting outcomes is that AE-FBCSP remarkably increased the number of subjects that responded with a very high accuracy, which is a fundamental requirement for BCI systems to be applied in practice.}, }
@article {pmid37022448, year = {2023}, author = {Gu, Y and Zhong, X and Qu, C and Liu, C and Chen, B}, title = {A Domain Generative Graph Network for EEG-based Emotion Recognition.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3242090}, pmid = {37022448}, issn = {2168-2208}, abstract = {Emotion is a human attitude experience and corresponding behavioral response to objective things. Effective emotion recognition is important for the intelligence and humanization of brain-computer interface (BCI). Although deep learning has been widely used in emotion recognition in recent years, emotion recognition based on electroencephalography (EEG) is still a challenging task in practical applications. Herein, we proposed a novel hybrid model that employs generative adversarial networks to generate potential representations of EEG signals while combining graph convolutional neural networks and long short-term memory networks to recognize emotions from EEG signals. Experimental results on DEAP and SEED datasets show that the proposed model achieved the promising emotion classification performance compared with the state-of-the-art methods.}, }
@article {pmid37022416, year = {2023}, author = {Wu, X and Jiang, S and Li, G and Liu, S and Metcalfe, B and Chen, L and Zhang, D}, title = {Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG).}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3242262}, pmid = {37022416}, issn = {2168-2208}, abstract = {OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals.<br><br>METHODS: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN.<br><br>RESULTS: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 &#xb1; 6.1%, 38 &#xb1; 4.9%, 60 &#xb1; 3.9%, 60 &#xb1; 3.3%, 61 &#xb1; 3.2%, and 63 &#xb1; 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain.<br><br>CONCLUSION: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives.<br><br>SIGNIFICANCE: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.}, }
@article {pmid37022411, year = {2023}, author = {Tang, X and Yang, C and Sun, X and Zou, M and Wang, H}, title = {Motor Imagery EEG Decoding Based on Multi-scale Hybrid Networks and Feature Enhancement.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3242280}, pmid = {37022411}, issn = {1558-0210}, abstract = {Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain's intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What's more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG signals' advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.}, }
@article {pmid37022389, year = {2023}, author = {Li, C and Li, P and Zhang, Y and Li, N and Si, Y and Li, F and Cao, Z and Chen, H and Chen, B and Yao, D and Xu, P}, title = {Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3238519}, pmid = {37022389}, issn = {2162-2388}, abstract = {Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.}, }
@article {pmid37022370, year = {2023}, author = {Li, M and Li, N and Gao, X and Ma, R and Dong, J and Chen, X and Cui, H}, title = {A Novel SSVEP Brain-Computer Interface System Based on Simultaneous Modulation of Luminance and Motion.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3241629}, pmid = {37022370}, issn = {1558-0210}, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have received significant attention owing to their high information transfer rate (ITR) and low training requirements. Previous SSVEP-based BCIs mostly adopt the stationary visual flickers where only a few studies have explored the effect of moving visual flickers on the SSVEP-BCI. In this study, a novel stimulus encoding method based on the simultaneous modulation of luminance and motion was proposed. We adopted the sampled sinusoidal stimulation method to encode the frequencies and phases of stimulus targets. In addition to luminance modulation, at the same time, visual flickers also moved horizontally towards right and left at different frequencies (i.e., 0, 0.2, 0.4, and 0.6 Hz) following a sinusoidal function. Accordingly, a nine-target SSVEP-BCI was built to evaluate the influence of motion modulation on the BCI performance. Filter bank canonical correlation analysis (FBCCA) approach was adopted to identify the stimulus targets. Offline experimental results of 17 subjects revealed that the system performance decreased with the increase of superimposed horizontal periodic motion frequency. Our online experimental results showed that the subjects achieved 85.00 ± 6.77 % and 83.15 ± 9.88 % accuracy for the superimposed horizontal periodic motion frequencies of 0 and 0.2 Hz, respectively. These results verified the feasibility of the proposed systems. In addition, the system with 0.2 Hz horizontal motion frequency provided the best visual experience for subjects. These results indicated that moving visual stimulus can provide an alternative option for SSVEP-BCIs. Furthermore, the proposed paradigm is expected to develop a more comfortable BCI system.}, }
@article {pmid37022367, year = {2023}, author = {Gao, Y and Li, M and Peng, Y and Fang, F and Zhang, Y}, title = {Double Stage Transfer Learning for Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3241301}, pmid = {37022367}, issn = {1558-0210}, abstract = {In the application of brain-computer interfaces (BCIs), electroencephalogram (EEG) signals are difficult to collect in large quantities due to the non-stationary nature and long calibration time required. Transfer learning (TL), which transfers knowledge learned from existing subjects to new subjects, can be applied to solve this problem. Some existing EEG-based TL algorithms cannot achieve good results because they only extract partial features. To achieve effective transfer, a double-stage transfer learning (DSTL) algorithm which applied transfer learning to both preprocessing stage and feature extraction stage of typical BCIs was proposed. First, Euclidean alignment (EA) was used to align EEG trials from different subjects. Second, aligned EEG trials in the source domain were reweighted by the distance between the covariance matrix of each trial in the source domain and the mean covariance matrix of the target domain. Lastly, after extracting spatial features with common spatial patterns (CSP), transfer component analysis (TCA) was adopted to reduce the differences between different domains further. Experiments on two public datasets in two transfer paradigms (multi-source to single-target (MTS) and single-source to single-target (STS)) verified the effectiveness of the proposed method. The proposed DSTL achieved better classification accuracy on two datasets: 84.64% and 77.16% in MTS, 73.38% and 68.58% in STS, which shows that DSTL performs better than other state-of-the-art methods. The proposed DSTL can reduce the difference between the source domain and the target domain, providing a new method for EEG data classification without training dataset.}, }
@article {pmid37022366, year = {2023}, author = {She, Q and Chen, T and Fang, F and Zhang, J and Gao, Y and Zhang, Y}, title = {Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3241846}, pmid = {37022366}, issn = {1558-0210}, abstract = {Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in brain-computer interface (BCI) technology have facilitated the detection of MI from electroencephalogram (EEG). Previous studies have proposed various EEG-based classification algorithms to identify the MI, however, the performance of prior models was limited due to the cross-subject heterogeneity in EEG data and the shortage of EEG data for training. Therefore, inspired by generative adversarial network (GAN), this study aims to propose an improved domain adaption network based on Wasserstein distance, which utilizes existing labeled data from multiple subjects (source domain) to improve the performance of MI classification on a single subject (target domain). Specifically, our proposed framework consists of three components, including a feature extractor, a domain discriminator, and a classifier. The feature extractor employs an attention mechanism and a variance layer to improve the discrimination of features extracted from different MI classes. Next, the domain discriminator adopts the Wasserstein matrix to measure the distance between source domain and target domain, and aligns the data distributions of source and target domain via adversarial learning strategy. Finally, the classifier uses the knowledge acquired from the source domain to predict the labels in the target domain. The proposed EEG-based MI classification framework was evaluated by two open-source datasets, the BCI Competition IV Datasets 2a and 2b. Our results demonstrated that the proposed framework could enhance the performance of EEG-based MI detection, achieving better classification results compared with several state-of-the-art algorithms. In conclusion, this study is promising in helping the neural rehabilitation of different neuropsychiatric diseases.}, }
@article {pmid37022234, year = {2023}, author = {Pham, TD}, title = {Classification of Motor-Imagery Tasks using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3241241}, pmid = {37022234}, issn = {1558-0210}, abstract = {Brain-computer or brain-machine interface technology allows humans to control machines using their thoughts via brain signals. In particular, these interfaces can assist people with neurological diseases for speech understanding or physical disabilities for operating devices such as wheelchairs. Motor-imagery tasks play a basic role in brain-computer interfaces. This study introduces an approach for classifying motor-imagery tasks in a brain-computer interface environment, which remains a challenge for rehabilitation technology using electroencephalogram sensors. Methods used and developed for addressing the classification include wavelet time and image scattering networks, fuzzy recurrence plots, support vector machines, and classifier fusion. The rationale for combining outputs from two classifiers learning on wavelet-time and wavelet-image scattering features of brain signals, respectively, is that they are complementary and can be effectively fused using a novel fuzzy rule-based system. A large-scale challenging electroencephalogram dataset of motor imagery-based brain-computer interface was used to test the efficacy of the proposed approach. Experimental results obtained from within-session classification show the potential application of the new model that achieves an improvement of 7% in classification accuracy over the best existing classifier using state-of-the-art artificial intelligence (76% versus 69%, respectively). For the cross-session experiment, which imposes a more challenging and practical classification task, the proposed fusion model improves the accuracy by 11% (54% versus 65%). The technical novelty presented herein and its further exploration are promising for developing a reliable sensor-based intervention for assisting people with neurodisability to improve their quality of life.}, }
@article {pmid37022076, year = {2023}, author = {Kwak, Y and Kong, K and Song, WJ and Kim, SE}, title = {Subject-Invariant Deep Neural Networks based on Baseline Correction for EEG Motor Imagery BCI.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3238421}, pmid = {37022076}, issn = {2168-2208}, abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) systems have been extensively used in various applications, such as communication, control, and rehabilitation. However, individual anatomical and physiological differences cause subject-specific variability of EEG signals for the same task, and BCI systems thus require a calibration procedure that adjusts system parameters to each subject. To overcome this problem, we propose a subject-invariant deep neural network (DNN) using baseline-EEG signals that can be recorded from subjects resting in comfortable states. We first modeled the deep features of EEG signals as a decomposition of subject-invariant and subject-variant features corrupted by anatomical/physiological characteristics. Subject-variant features were then removed from the deep features by learning the network with a baseline correction module (BCM) using the underlying individual information in baseline-EEG signals. The subject-invariant loss forces the BCM to assemble subject-invariant features that have the same class, irrespective of the subject. Using 1-min baseline-EEG signals of the new subject, our algorithm can eliminate subject-variant components from test data without the calibration process. The experimental results show that our subject-invariant DNN framework significantly increases decoding accuracies of the conventional DNN methods for BCI systems. Furthermore, feature visualizations illustrate that the proposed BCM extracts subject-invariant features that are close to each other in the same class.}, }
@article {pmid37022027, year = {2023}, author = {Grover, N and Chharia, A and Upadhyay, R and Longo, L}, title = {Schizo-Net: A novel Schizophrenia Diagnosis framework using late fusion multimodal deep learning on Electroencephalogram-based Brain connectivity indices.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3237375}, pmid = {37022027}, issn = {1558-0210}, abstract = {Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject's interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net, a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity, and six different deep learning architectures (with varying neurons and hidden layers) are trained. The present study is the first which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity, and the vital significance of BCI is drawn in this regard to identify the biomarkers of the disease. Schizo-Net surpasses current models and achieves 99.84% accuracy. An optimum deep learning architecture selection is also performed for improved classification. The study also establishes that Late fusion technique outperforms single architecture-based prediction in diagnosing SCZ.}, }
@article {pmid37021989, year = {2023}, author = {Ding, Y and Robinson, N and Tong, C and Zeng, Q and Guan, C}, title = {LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3236635}, pmid = {37021989}, issn = {2162-2388}, abstract = {Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-global-graph network (LGGNet), a novel neurologically inspired graph neural network (GNN), to learn local-global-graph (LGG) representations of electroencephalography (EEG) for brain-computer interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multiscale 1-D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local-and global-graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art (SOTA) methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, regularized graph neural network (RGNN), attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN), hierarchical recurrent neural network (HRNN), and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant () in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG.}, }
@article {pmid37021904, year = {2023}, author = {Wang, Z and Chen, C and Li, J and Wan, F and Sun, Y and Wang, H}, title = {ST-CapsNet: Linking Spatial and Temporal Attention with Capsule Network for P300 Detection Improvement.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3237319}, pmid = {37021904}, issn = {1558-0210}, abstract = {A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 detection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300.}, }
@article {pmid37021903, year = {2023}, author = {Park, S and Ha, J and Kim, L}, title = {Improving Performance of Motor Imagery-based Brain-computer Interface in Poorly Performing Subjects Using a Hybrid-imagery Method utilizing Combined Motor and Somatosensory Activity.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3237583}, pmid = {37021903}, issn = {1558-0210}, abstract = {The phenomena of brain-computer interface-inefficiency in transfer rates and reliability can hinder development and use of brain-computer interface technology. This study aimed to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers using a hybrid-imagery approach that combined motor and somatosensory activity. Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybrid-condition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball). The three paradigms for all participants, achieved an average accuracy of 63.60±21.62%, 71.25±19.53%, and 84.09±12.79% using the filter bank common spatial pattern algorithm (5-fold cross-validation), respectively. In the poor performance group, the Hybrid-condition II paradigm achieved an accuracy of 81.82%, showing a significant increase of 38.86% and 21.04% in accuracy compared to the control-condition (42.96%) and Hybrid-condition I (60.78%), respectively. Conversely, the good performance group showed a pattern of increasing accuracy, with no significant difference between the three paradigms. The Hybrid-condition II paradigm provided high concentration and discrimination to poor performers in the motor imagery-based brain-computer interface and generated the enhanced event-related desynchronization pattern in three modalities corresponding to different types of somatosensory stimuli in motor and somatosensory regions compared to the Control-condition and Hybrid-condition I. The hybrid-imagery approach can help improve motor imagery-based brain-computer interface performance, especially for poorly performing users, thus contributing to the practical use and uptake of brain-computer interface.}, }
@article {pmid37021872, year = {2022}, author = {Abrams, Z}, title = {The Future of Brain-Computer Interfaces.}, journal = {IEEE pulse}, volume = {13}, number = {6}, pages = {21-24}, doi = {10.1109/MPULS.2022.3227806}, pmid = {37021872}, issn = {2154-2317}, abstract = {Operating a drone, playing video games, or controlling a robot simply by thinking are exciting applications of brain-computer interfaces (BCIs) that pave the way for more mind-bending breakthroughs. Crucially, BCIs, which enable the brain to exchange signals with an outside device, also represent a powerful tool to restore movement, speech, touch, and other functions to patients with brain damage. Despite recent progress in the field, technological innovation is needed and plenty of scientific and ethical questions remain unanswered. Still, researchers say BCIs hold great promise for patients with the most severe impairments-and that major breakthroughs are within reach.}, }
@article {pmid37021322, year = {2023}, author = {Nyawanda, BO and Beloconi, A and Khagayi, S and Bigogo, G and Obor, D and Otieno, NA and Lange, S and Franke, J and Sauerborn, R and Utzinger, J and Kariuki, S and Munga, S and Vounatsou, P}, title = {The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: A time-series analysis of monthly incidence data from 2008 to 2019.}, journal = {Parasite epidemiology and control}, volume = {21}, number = {}, pages = {e00297}, pmid = {37021322}, issn = {2405-6731}, abstract = {BACKGROUND: Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya.<br><br>METHODS: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence.<br><br>RESULTS: Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR]&#xa0;=&#xa0;0.70, 95% Bayesian credible interval [BCI]: 0.59-0.82), while rainfall was associated with increased incidence (IRR&#xa0;=&#xa0;1.27, 95% BCI: 1.10-1.44). Bed net use was associated with a decline in malaria incidence in children aged 6-59&#xa0;months (IRR&#xa0;=&#xa0;0.78, 95% BCI: 0.70-0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population.<br><br>CONCLUSIONS: Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6-59&#xa0;months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.}, }
@article {pmid37019099, year = {2023}, author = {Bryan, MJ and Jiang, LP and Rao, RPN}, title = {Neural co-processors for restoring brain function:Results from a cortical model of grasping.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/accaa9}, pmid = {37019099}, issn = {1741-2552}, abstract = {OBJECTIVE: A major challenge in designing closed-loop brain-computer interfaces (BCIs) is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Traditional approaches, such as those currently used for deep brain stimulation (DBS), have largely followed a manual trial-and-error strategy to search for effective open-loop stimulation parameters, a strategy that is inefficient and does not generalize to closed-loop activity-dependent stimulation.<br><br>APPROACH: To achieve goal-directed closed-loop neurostimulation, we propose the use of neural co-processors, devices which use artificial neural networks (ANNs) and deep learning to learn optimal closed-loop stimulation policies to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for future in vivo tests of neural co-processors. We leverage a previously published cortical model of grasping, to which we applied various forms of simulated lesions. We used our simulations to develop the critical learning algorithms and study adaptations to non-stationarity.<br><br>MAIN RESULTS: Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied.<br><br>SIGNIFICANCE: Our results provide the first proof-of-concept demonstration, using computer simulations, of a neural co-processor for adaptive activity-dependent closed-loop neurostimulation for optimizing a rehabilitation goal after injury. While a significant gap remains between simulations and in vivo applications, our results provide insights on how such co-processors may eventually be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications.}, }
@article {pmid37018725, year = {2023}, author = {Wei, Q and Ding, X}, title = {Intra- and inter-subject common spatial pattern for reducing calibration effort in MI-based BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3236372}, pmid = {37018725}, issn = {1558-0210}, abstract = {One major problem limiting the practicality of a brain-computer interface (BCI) is the need for large amount of labeled data to calibrate its classification model. Although the effectiveness of transfer learning (TL) for conquering this problem has been evidenced by many studies, a highly recognized approach has not yet been established. In this paper, we propose a Euclidean alignment (EA)-based Intra- and inter-subject common spatial pattern (EA-IISCSP) algorithm for estimating four spatial filters, which aim at exploiting Intra- and inter-subject similarities and variability to enhance the robustness of feature signals. Based on the algorithm, a TL-based classification framework was developed for enhancing the performance of motor imagery (MI) BCIs, in which the feature vector extracted by each filter is dimensionally reduced by linear discriminant analysis (LDA) and a support vector machine (SVM) is used for classification. The performance of the proposed algorithm was evaluated on two MI data sets and compared with that of three state-of-the-art TL algorithms. Experimental results showed that the proposed algorithm significantly outperforms these competing algorithms for training trials per class from 15 to 50 and can reduce the amount of training data while maintaining an acceptable accuracy, thus facilitating the practical application of MI-based BCIs.}, }
@article {pmid37018712, year = {2023}, author = {Naser, MYM and Bhattacharya, S}, title = {Towards Practical BCI-Driven Wheelchairs: A Systematic Review Study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3236251}, pmid = {37018712}, issn = {1558-0210}, abstract = {The use of brain signals in controlling wheelchairs is a promising solution for many disabled individuals, specifically those who are suffering from motor neuron disease affecting the proper functioning of their motor units. Almost two decades since the first work, the applicability of EEG-driven wheelchairs is still limited to laboratory environments. In this work, a systematic review study has been conducted to identify the state-of-the-art and the different models adopted in the literature. Furthermore, a strong emphasis is devoted to introducing the challenges impeding a broad use of the technology as well as the latest research trends in each of those areas.}, }
@article {pmid37018635, year = {2023}, author = {Wang, X and Liu, A and Wu, L and Li, C and Liu, Y and Chen, X}, title = {A Generalized Zero-Shot Learning Scheme for SSVEP-Based BCI System.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3235804}, pmid = {37018635}, issn = {1558-0210}, abstract = {The steady-state visual evoked potential (SSVEP) has been widely used in building multi-target brain-computer interfaces (BCIs) based on electroencephalogram (EEG). However, methods for high-accuracy SSVEP systems require training data for each target, which needs significant calibration time. This study aimed to use the data of only part of the targets for training while achieving high classification accuracy on all targets. In this work, we proposed a generalized zero-shot learning (GZSL) scheme for SSVEP classification. We divided the target classes into seen and unseen classes and trained the classifier only using the seen classes. During the test time, the search space contained both seen classes and unseen classes. In the proposed scheme, the EEG data and the sine waves are embedded into the same latent space using convolutional neural networks (CNN). We use the correlation coefficient of the two outputs in the latent space for classification. Our method was tested on two public datasets and reached 89.9% of the classification accuracy of the state-of-the-art (SOTA) data-driven method, which needs the training data of all targets. Compared to the SOTA training-free method, our method achieved a multifold improvement. This work shows that it is promising to build an SSVEP classification system that does not need the training data of all targets.}, }
@article {pmid37018580, year = {2023}, author = {Zeng, H and Shen, Y and Sun, D and Hu, X and Wen, P and Liu, J and Song, A}, title = {Extended Control with Hybrid Gaze-BCI for Multi-Robot System under Hands-occupied Dual-tasking.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3234971}, pmid = {37018580}, issn = {1558-0210}, abstract = {Currently there still remains a critical need of human involvements for multi-robot system (MRS) to successfully perform their missions in real-world applications, and the hand-controller has been commonly used for the operator to input MRS control commands. However, in more challenging scenarios involving concurrent MRS control and system monitoring tasks, where the operator's both hands are busy, the hand-controller alone is inadequate for effective human-MRS interaction. To this end, our study takes a first step toward a multimodal interface by extending the hand-controller with a hands-free input based on gaze and brain-computer interface (BCI), i.e., a hybrid gaze-BCI. Specifically, the velocity control function is still designated to the hand-controller that excels at inputting continuous velocity commands for MRS, while the formation control function is realized with a more intuitive hybrid gaze-BCI, rather than with the hand-controller via a less natural mapping. In a dual-task experimental paradigm that simulated the hands-occupied manipulation condition in real-world applications, operators achieved improved performance for controlling simulated MRS (average formation inputting accuracy increases 3%, average finishing time decreases 5 s), reduced cognitive load (average reaction time for secondary task decreases 0.32 s) and perceived workload (average rating score decreases 15.84) with the hand-controller extended by the hybrid gaze-BCI, over those with the hand-controller alone. These findings reveal the potential of the hands-free hybrid gaze-BCI to extend the traditional manual MRS input devices for creating a more operator-friendly interface, in challenging hands-occupied dual-tasking scenarios.}, }
@article {pmid37018579, year = {2023}, author = {Wu, D and Shi, Y and Wang, Z and Yang, J and Sawan, M}, title = {C[2]SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3235390}, pmid = {37018579}, issn = {1558-0210}, abstract = {Recent developments in brain-machine inter-face technology have rendered seizure prediction possible. However, the transmission of a large volume of electro-physiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computational resources, especially for power-critical wearable and implantable medical devices. Although many data compression methods can be adopted to compress the signals to reduce communication bandwidth requirement, they require complex compression and reconstruction procedures before the signal can be used for seizure prediction. In this paper, we propose C[2]SP-Net, a framework to jointly solve compression, prediction, and reconstruction without extra computation overhead. The framework consists of a plug-and-play in-sensor compression matrix to reduce transmission bandwidth requirements. The compressed signal can be utilized for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Compression and classification overhead from the energy consumption perspective, prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated using various compression ratios. The experimental results illustrate that our proposed framework is energy efficient and outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.6% in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.}, }
@article {pmid37015706, year = {2022}, author = {Wang, H and Zheng, H and Wu, H and Long, J}, title = {Behavior-Dependent Corticocortical Contributions to Imagined Grasping: a BCI-triggered TMS study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3227511}, pmid = {37015706}, issn = {1558-0210}, abstract = {Previous studies have indicated that corticocortical neural mechanisms differ during various grasping behaviors. However, the literature rarely considers corticocortical contributions to various imagined grasping behaviors. To address this question, we examine their mechanisms by transcranial magnetic stimulation (TMS) triggered when detecting event-related desynchronization during right-hand grasping behavior imagination through a brain-computer interface (BCI) system. Based on the BCI system, we designed two experiments. In Experiment 1, we explored differences in motor evoked potentials (MEPs) between power grip and resting conditions. In Experiment 2, we used the three TMS coil orientations (lateral-medial (LM), posterior-anterior (PA), and anterior-posterior (AP) directions) over the primary motor cortex to elicit MEPs during imagined index finger abduction, precision grip, and power grip. We found that larger MEP amplitudes and shorter latencies were obtained in imagined power grip than in resting.We also detected lower MEP amplitudes during imagined power grip, while MEP amplitudes remained similar across imagined precision grip and index finger abduction in each TMS coil orientation. Differences in AP-LM latency were longer when subjects imagined a power grip compared with precision grip and index finger abduction. Based on our results, higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. Our results suggests that higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. We also propose that preferential recruitment of late synaptic inputs to corticospinal neurons may occur when humans imagine a power grip.}, }
@article {pmid37015688, year = {2022}, author = {Ahn, HJ and Lee, DH and Jeong, JH and Lee, SW}, title = {Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3229330}, pmid = {37015688}, issn = {1558-0210}, abstract = {A new kind of sequence-to-sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG-based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery-based brain-computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi-head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.}, }
@article {pmid37015587, year = {2022}, author = {Luo, R and Xu, M and Zhou, X and Xiao, X and Jung, TP and Ming, D}, title = {Data augmentation of SSVEPs using source aliasing matrix estimation for brain-computer interfaces.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2022.3227036}, pmid = {37015587}, issn = {1558-2531}, abstract = {OBJECTIVE: Currently, ensemble task-related component analysis (eTRCA) and task discriminative component analysis (TDCA) are the state-of-the-art algorithms for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, training the BCIs requires multiple calibration trials. With insufficient calibration data, the accuracy of the BCI will degrade, or even become invalid with only one calibration trial. However, collecting a large amount of electroencephalography (EEG) data for calibration is a time-consuming and laborious process, which hinders the practical use of eTRCA and TDCA.<br><br>METHODS: This study proposed a novel method, namely Source Aliasing Matrix Estimation (SAME), to augment the calibration data for SSVEP-BCIs. SAME could generate artificial EEG trials with the featured SSVEPs. Its effectiveness was evaluated using two public datasets (i.e., Benchmark, BETA).<br><br>RESULTS: When combined with SAME, both eTRCA and TDCA had significantly improved performance with a limited number of calibration data. Specifically, SAME increased the average accuracy of eTRCA and TDCA by about 12% and 3%, respectively, with as few as two calibration trials. Notably, SAME enabled eTRCA and TDCA to work well with a single calibration trial, achieving an average accuracy >90% for the Benchmark dataset and >70% for the BETA dataset with 1-second EEG.<br><br>CONCLUSION: SAME is an effective method for SSVEP-BCIs to augment the calibration data, thereby significantly enhancing the performance of eTRCA and TDCA.<br><br>SIGNIFICANCE: We propose a new data-augmentation method that is compatible with the state-of-the-art algorithms of SSVEP-based BCIs. It can significantly reduce the efforts required to calibrate SSVEP-BCIs, which is promising for the development of practical BCIs.}, }
@article {pmid37015471, year = {2022}, author = {Park, S and Ha, J and Park, J and Lee, K and Im, CH}, title = {Brain-Controlled, AR-based Home Automation System using SSVEP-based Brain-Computer Interface and EOG-based Eye Tracker: A Feasibility Study for the Elderly End User.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3228124}, pmid = {37015471}, issn = {1558-0210}, abstract = {Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user's preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.}, }
@article {pmid37015470, year = {2022}, author = {Guo, Z and Chen, F}, title = {Decoding Articulation Motor Imagery using Early Connectivity Information in the Motor Cortex: A Functional Near-infrared Spectroscopy Study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3227595}, pmid = {37015470}, issn = {1558-0210}, abstract = {Brain computer interface (BCI) based on speech imagery can help people with motor disorders communicate their thoughts to the outside world in a natural way. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) is preferred for developing BCIs. Previous BCIs based on fNIRS mainly relied on activation information, which ignored the functional connectivity between neural areas. In this study, a 4-class speech imagery BCI based on fNIRS is presented to decode simplified articulation motor imagery (only the movements of jaw and lip were retained) of different vowels. Synchronization information in the motor cortex was extracted as features. In multiclass (four classes) settings, the mean subject-dependent classification accuracies approximated or exceeded 40% in the 0-2.5 s and 0-10 s time windows, respectively. In binary class settings (the average classification accuracies of all pairwise comparisons between two vowels), the mean subject-dependent classification accuracies exceeded 70% in the 0-2.5 s and 0-10 s time windows. These results demonstrate that connectivity features can effectively differentiate different vowels even if the time window size was reduced from 10 s to 2.5 s and the decoding performance in both the time windows was almost the same. This finding suggests that speech imagery BCI based on fNIRS can be further optimized in terms of feature extraction and command generation time reduction. In addition, simplified articulation motor imagery of vowels can be distinguished, and therefore, the potential contribution of articulation motor imagery information extracted from the motor cortex should be emphasized in speech imagery BCI based on fNIRS to improve decoding performance.}, }
@article {pmid37015468, year = {2022}, author = {Huang, X and Liang, S and Zhang, Y and Zhou, N and Pedrycz, W and Choi, KS}, title = {Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3228216}, pmid = {37015468}, issn = {1558-0210}, abstract = {For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects.}, }
@article {pmid37015437, year = {2022}, author = {Zou, B and Zheng, Y and Shen, M and Luo, Y and Li, L and Zhang, L}, title = {BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2022.3230500}, pmid = {37015437}, issn = {1940-9990}, abstract = {Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition systems' hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4 kHz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. There are no packet losses and the average maximum delay is only 0.07 s/h. Moreover, as an open-source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced. Schematics, source code, and other materials of BEATS are available at https://github.com/buptantEEG/BEATS.}, }
@article {pmid37015133, year = {2023}, author = {Canal, G and Diaz-Mercado, Y and Egerstedt, M and Rozell, C}, title = {A Low-Complexity Brain-Computer Interface for High-Complexity Robot Swarm Control.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {1816-1825}, doi = {10.1109/TNSRE.2023.3257261}, pmid = {37015133}, issn = {1558-0210}, abstract = {A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user's brain activity, which is then processed into commands that drive a system end effector. BCIs involve either invasive measurements which allow for high-complexity control but are generally infeasible, or noninvasive measurements which offer lower quality signals but are more practical to use. In general, BCI systems have not been developed that efficiently, robustly, and scalably perform high-complexity control while retaining the practicality of noninvasive measurements. Here we leverage recent results from feedback information theory to fill this gap by modeling BCIs as a communications system and deploying a human-implementable interaction algorithm for noninvasive control of a high-complexity robot swarm. We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models. Our results provide a proof of concept for how a large class of high-complexity effectors (even beyond robotics) can be effectively controlled by a BCI system with low-complexity and noisy inputs.}, }
@article {pmid37015116, year = {2023}, author = {Luo, X and Lin, Y and Guo, R and Gao, X and Zhang, S}, title = {ERP and pupillometry synchronization analysis on Rapid Serial Visual Presentation of words, numbers, pictures.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3263502}, pmid = {37015116}, issn = {1558-0210}, abstract = {Hybrid brain-computer interfaces (HBCI) combining eye-tracker has attracted the attentions of researchers in target recognition. However, there are still many issues to be addressed in rapid sequence visual presentation (RSVP) tasks, such as the effect of presentation rates and target types on event-related potentials (ERP) and pupillometry, synchronization analysis of electroencephalography (EEG) and eye-tracking, and so on. In this study, the RSVP experiments with three different target types of pictures, words and numbers at the presentation rates of 100 and 200 ms were conducted. EEG data and pupillometry data were synchronously collected from 20 university students. The results of ERP analysis showed that, among three different target types at the presentation rate of 100 ms, the picture P300 component had the largest amplitude and the longest latency. From the 100 ms presentation rates to 200 ms one for the three target types, the P300 amplitudes became smaller, and the P300 latencies became shorter. The results of pupillometry analysis showed that, at the presentation rates of 100 and 200 ms, the pupil dilation of pictures had the smallest amplitude and the shortest latency. At the two presentation rates, no significant differences of pupil size and latency were found for the three target types. For the early pupil dilation within 1000 ms, the picture pupil size was significantly smaller than the other ones, and the picture pupil acceleration had the largest average amplitude and the shortest latency. These pupillometry features within 1000 ms combining with the P300 features could be taken as the effective ones for target classification. Through synchronization analysis of the EEG data and pupillometry data, the effects of target type and presentation rate on ERP and pupil dilation were different. These results could contribute to developing the fusion methods between EEG and eye-tracking, and provide valuable references for the multi-target recognition of hybrid BCI based on eye-tracking.}, }
@article {pmid37015115, year = {2023}, author = {Wang, X and Ma, Y and Cammon, J and Fang, F and Gao, Y and Zhang, Y}, title = {Self-supervised EEG emotion recognition models based on CNN.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3263570}, pmid = {37015115}, issn = {1558-0210}, abstract = {Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on emotion classification show resource utilization because they use the fully-supervised learning methods. Therefore, in this study, we applied the self-supervised learning methods to improve the efficiency of resources usage. We employed a self-supervised approach to train deep multi-task convolutional neural network (CNN) for EEG-based emotion classification. First, six signal transformations were performed on unlabeled EEG data to construct the pretext task. Second, a multi-task CNN was used to perform signal transformation recognition on the transformed signals together with the original signals. After the signal transformation recognition network was trained, the convolutional layer network was frozen and the fully connected layer was reconstructed as emotion recognition network. Finally, the EEG data with affective labels were used to train the emotion recognition network to clarify the emotion. In this paper, we conduct extensive experiments from the data scaling perspective using the SEED, DEAP affective dataset. Results showed that the self-supervised learning methods can learn the internal representation of data and save computation time compared to the fully-supervised learning methods. In conclusion, our study suggests that the self-supervised machine learning model can improve the performance of emotion classification compared to the conventional fully supervised model.}, }
@article {pmid37012508, year = {2023}, author = {Cadoni, S and Demené, C and Alcala, I and Provansal, M and Nguyen, D and Nelidova, D and Labernède, G and Lubetzki, J and Goulet, R and Burban, E and Dégardin, J and Simonutti, M and Gauvain, G and Arcizet, F and Marre, O and Dalkara, D and Roska, B and Sahel, JA and Tanter, M and Picaud, S}, title = {Ectopic expression of a mechanosensitive channel confers spatiotemporal resolution to ultrasound stimulations of neurons for visual restoration.}, journal = {Nature nanotechnology}, volume = {}, number = {}, pages = {}, pmid = {37012508}, issn = {1748-3395}, abstract = {Remote and precisely controlled activation of the brain is a fundamental challenge in the development of brain-machine interfaces for neurological treatments. Low-frequency ultrasound stimulation can be used to modulate neuronal activity deep in the brain, especially after expressing ultrasound-sensitive proteins. But so far, no study has described an ultrasound-mediated activation strategy whose spatiotemporal resolution and acoustic intensity are compatible with the mandatory needs of brain-machine interfaces, particularly for visual restoration. Here we combined the expression of large-conductance mechanosensitive ion channels with uncustomary high-frequency ultrasonic stimulation to activate retinal or cortical neurons over millisecond durations at a spatiotemporal resolution and acoustic energy deposit compatible with vision restoration. The in vivo sonogenetic activation of the visual cortex generated a behaviour associated with light perception. Our findings demonstrate that sonogenetics can deliver millisecond pattern presentations via an approach less invasive than current brain-machine interfaces for visual restoration.}, }
@article {pmid37010783, year = {2023}, author = {Phillips, MM and Pavlyk, I and Allen, M and Ghazaly, E and Cutts, R and Carpentier, J and Berry, JS and Nattress, C and Feng, S and Hallden, G and Chelala, C and Bomalaski, J and Steele, J and Sheaff, M and Balkwill, F and Szlosarek, PW}, title = {A role for macrophages under cytokine control in mediating resistance to ADI-PEG20 (pegargiminase) in ASS1-deficient mesothelioma.}, journal = {Pharmacological reports : PR}, volume = {}, number = {}, pages = {}, pmid = {37010783}, issn = {2299-5684}, support = {MRCCRTF11-8/MRC_/Medical Research Council/United Kingdom ; C12522/A8632/CRUK_/Cancer Research UK/United Kingdom ; }, abstract = {BACKGROUND: Pegylated arginine deiminase (ADI-PEG20; pegargiminase) depletes arginine and improves survival outcomes for patients with argininosuccinate synthetase 1 (ASS1)-deficient malignant pleural mesothelioma (MPM). Optimisation of ADI-PEG20-based therapy will require a deeper understanding of resistance mechanisms, including those mediated by the tumor microenvironment. Here, we sought to reverse translate increased tumoral macrophage infiltration in patients with ASS1-deficient MPM relapsing on pegargiminase therapy.<br><br>METHODS: Macrophage-MPM tumor cell line (2591, MSTO, JU77) co-cultures treated with ADI-PEG20 were analyzed by flow cytometry. Microarray experiments of gene expression profiling were performed in ADI-PEG20-treated MPM tumor cells, and macrophage-relevant genetic "hits" were validated by qPCR, ELISA, and LC/MS. Cytokine and argininosuccinate analyses were performed using plasma from pegargiminase-treated patients with MPM.<br><br>RESULTS: We identified that ASS1-expressing macrophages promoted viability of ADI-PEG20-treated ASS1-negative MPM cell lines. Microarray gene expression data revealed a dominant CXCR2-dependent chemotactic signature and co-expression of VEGF-A and IL-1&#x3b1; in ADI-PEG20-treated MPM cell lines. We confirmed that ASS1 in macrophages was IL-1&#x3b1;-inducible and that the argininosuccinate concentration doubled in the cell supernatant sufficient to restore MPM cell viability under co-culture conditions with ADI-PEG20. For further validation, we detected elevated plasma VEGF-A and CXCR2-dependent cytokines, and increased argininosuccinate in patients with MPM progressing on ADI-PEG20. Finally, liposomal clodronate depleted ADI-PEG20-driven macrophage infiltration and suppressed growth significantly in the MSTO xenograft murine model.<br><br>CONCLUSIONS: Collectively, our data indicate that ADI-PEG20-inducible cytokines orchestrate argininosuccinate fuelling of ASS1-deficient mesothelioma by macrophages. This novel stromal-mediated resistance pathway may be leveraged to optimize arginine deprivation therapy for mesothelioma and related arginine-dependent cancers.}, }
@article {pmid37009261, year = {2023}, author = {Lyreskog, DM and Zohny, H and Savulescu, J and Singh, I}, title = {Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds.}, journal = {Neuroethics}, volume = {16}, number = {1}, pages = {12}, pmid = {37009261}, issn = {1874-5490}, abstract = {A growing number of technologies are currently being developed to improve and distribute thinking and decision-making. Rapid progress in brain-to-brain interfacing and swarming technologies promises to transform how we think about collective and collaborative cognitive tasks across domains, ranging from research to entertainment, and from therapeutics to military applications. As these tools continue to improve, we are prompted to monitor how they may affect our society on a broader level, but also how they may reshape our fundamental understanding of agency, responsibility, and other key concepts of our moral landscape. In this paper we take a closer look at this class of technologies - Technologies for Collective Minds - to see not only how their implementation may react with commonly held moral values, but also how they challenge our underlying concepts of what constitutes collective or individual agency. We argue that prominent contemporary frameworks for understanding collective agency and responsibility are insufficient in terms of accurately describing the relationships enabled by Technologies for Collective Minds, and that they therefore risk obstructing ethical analysis of the implementation of these technologies in society. We propose a more multidimensional approach to better understand this set of technologies, and to facilitate future research on the ethics of Technologies for Collective Minds.}, }
@article {pmid37008216, year = {2023}, author = {Minciacchi, D and Bravi, R and Rosenboom, D}, title = {Editorial: Sonification, aesthetic representation of physical quantities.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1162383}, pmid = {37008216}, issn = {1662-4548}, }
@article {pmid37008204, year = {2023}, author = {Bai, X and Li, M and Qi, S and Ng, ACM and Ng, T and Qian, W}, title = {A hybrid P300-SSVEP brain-computer interface speller with a frequency enhanced row and column paradigm.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1133933}, pmid = {37008204}, issn = {1662-4548}, abstract = {OBJECTIVE: This study proposes a new hybrid brain-computer interface (BCI) system to improve spelling accuracy and speed by stimulating P300 and steady-state visually evoked potential (SSVEP) in electroencephalography (EEG) signals.<br><br>METHODS: A frequency enhanced row and column (FERC) paradigm is proposed to incorporate the frequency coding into the row and column (RC) paradigm so that the P300 and SSVEP signals can be evoked simultaneously. A flicker (white-black) with a specific frequency from 6.0 to 11.5 Hz with an interval of 0.5 Hz is assigned to one row or column of a 6 &#xd7; 6 layout, and the row/column flashes are carried out in a pseudorandom sequence. A wavelet and support vector machine (SVM) combination is adopted for P300 detection, an ensemble task-related component analysis (TRCA) method is used for SSVEP detection, and the two detection possibilities are fused using a weight control approach.<br><br>RESULTS: The implemented BCI speller achieved an accuracy of 94.29% and an information transfer rate (ITR) of 28.64 bit/min averaged across 10 subjects during the online tests. An accuracy of 96.86% is obtained during the offline calibration tests, higher than that of only using P300 (75.29%) or SSVEP (89.13%). The SVM in P300 outperformed the previous linear discrimination classifier and its variants (61.90-72.22%), and the ensemble TRCA in SSVEP outperformed the canonical correlation analysis method (73.33%).<br><br>CONCLUSION: The proposed hybrid FERC stimulus paradigm can improve the performance of the speller compared with the classical single stimulus paradigm. The implemented speller can achieve comparable accuracy and ITR to its state-of-the-art counterparts with advanced detection algorithms.}, }
@article {pmid37007683, year = {2023}, author = {Proverbio, AM and Pischedda, F}, title = {Measuring brain potentials of imagination linked to physiological needs and motivational states.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1146789}, pmid = {37007683}, issn = {1662-5161}, abstract = {INTRODUCTION: While EEG signals reflecting motor and perceptual imagery are effectively used in brain computer interface (BCI) contexts, little is known about possible indices of motivational states. In the present study, electrophysiological markers of imagined motivational states, such as craves and desires were investigated.<br><br>METHODS: Event-related potentials (ERPs) were recorded in 31 participants during perception and imagery elicited by the presentation of 360 pictograms. Twelve micro-categories of needs, subdivided into four macro-categories, were considered as most relevant for a possible BCI usage, namely: primary visceral needs (e.g., hunger, linked to desire of food); somatosensory thermal and pain sensations (e.g., cold, linked to desire of warm), affective states (e.g., fear: linked to desire of reassurance) and secondary needs (e.g., desire to exercise or listen to music). Anterior N400 and centroparietal late positive potential (LPP) were measured and statistically analyzed.<br><br>RESULTS: N400 and LPP were differentially sensitive to the various volition stats, depending on their sensory, emotional and motivational poignancy. N400 was larger to imagined positive appetitive states (e.g., play, cheerfulness) than negative ones (sadness or fear). In addition, N400 was of greater amplitude during imagery of thermal and nociceptive sensations than other motivational or visceral states. Source reconstruction of electromagnetic dipoles showed the activation of sensorimotor areas and cerebellum for movement imagery, and of auditory and superior frontal areas for music imagery.<br><br>DISCUSSION: Overall, ERPs were smaller and more anteriorly distributed during imagery than perception, but showed some similarity in terms of lateralization, distribution, and category response, thus indicating some overlap in neural processing, as also demonstrated by correlation analyses. In general, anterior frontal N400 provided clear markers of subjects' physiological needs and motivational states, especially cold, pain, and fear (but also sadness, the urgency to move, etc.), than can signal life-threatening conditions. It is concluded that ERP markers might potentially allow the reconstruction of mental representations related to various motivational states through BCI systems.}, }
@article {pmid37007682, year = {2023}, author = {Jadavji, Z and Kirton, A and Metzler, MJ and Zewdie, E}, title = {BCI-activated electrical stimulation in children with perinatal stroke and hemiparesis: A pilot study.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1006242}, pmid = {37007682}, issn = {1662-5161}, abstract = {BACKGROUND: Perinatal stroke (PS) causes most hemiparetic cerebral palsy (CP) and results in lifelong disability. Children with severe hemiparesis have limited rehabilitation options. Brain computer interface- activated functional electrical stimulation (BCI-FES) of target muscles may enhance upper extremity function in hemiparetic adults. We conducted a pilot clinical trial to assess the safety and feasibility of BCI-FES in children with hemiparetic CP.<br><br>METHODS: Thirteen participants (mean age = 12.2 years, 31% female) were recruited from a population-based cohort. Inclusion criteria were: (1) MRI-confirmed PS, (2) disabling hemiparetic CP, (3) age 6-18 years, (4) informed consent/assent. Those with neurological comorbidities or unstable epilepsy were excluded. Participants attended two BCI sessions: training and rehabilitation. They wore an EEG-BCI headset and two forearm extensor stimulation electrodes. Participants' imagination of wrist extension was classified on EEG, after which muscle stimulation and visual feedback were provided when the correct visualization was detected.<br><br>RESULTS: No serious adverse events or dropouts occurred. The most common complaints were mild headache, headset discomfort and muscle fatigue. Children ranked the experience as comparable to a long car ride and none reported as unpleasant. Sessions lasted a mean of 87 min with 33 min of stimulation delivered. Mean classification accuracies were (M = 78.78%, SD = 9.97) for training and (M = 73.48, SD = 12.41) for rehabilitation. Mean Cohen's Kappa across rehabilitation trials was M = 0.43, SD = 0.29, range = 0.019-1.00, suggesting BCI competency.<br><br>CONCLUSION: Brain computer interface-FES was well -tolerated and feasible in children with hemiparesis. This paves the way for clinical trials to optimize approaches and test efficacy.}, }
@article {pmid37007675, year = {2023}, author = {Śliwowski, M and Martin, M and Souloumiac, A and Blanchart, P and Aksenova, T}, title = {Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1111645}, pmid = {37007675}, issn = {1662-5161}, abstract = {INTRODUCTION: In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as machine learning methods depend strongly on the training dataset size. Important questions arise: taking into account neuronal signal characteristics (e.g., non-stationarity), can we achieve higher decoding performance with more data to train decoders? What is the perspective for further improvement with time in the case of long-term BCI studies? In this study, we investigated the impact of long-term recordings on motor imagery decoding from two main perspectives: model requirements regarding dataset size and potential for patient adaptation.<br><br>METHODS: We evaluated the multilinear model and two deep learning (DL) models on a long-term BCI &amp; Tetraplegia (ClinicalTrials.gov identifier: NCT02550522) clinical trial dataset containing 43 sessions of ECoG recordings performed with a tetraplegic patient. In the experiment, a participant executed 3D virtual hand translation using motor imagery patterns. We designed multiple computational experiments in which training datasets were increased or translated to investigate the relationship between models' performance and different factors influencing recordings.<br><br>RESULTS: Our results showed that DL decoders showed similar requirements regarding the dataset size compared to the multilinear model while demonstrating higher decoding performance. Moreover, high decoding performance was obtained with relatively small datasets recorded later in the experiment, suggesting motor imagery patterns improvement and patient adaptation during the long-term experiment. Finally, we proposed UMAP embeddings and local intrinsic dimensionality as a way to visualize the data and potentially evaluate data quality.<br><br>DISCUSSION: DL-based decoding is a prospective approach in BCI which may be efficiently applied with real-life dataset size. Patient-decoder co-adaptation is an important factor to consider in long-term clinical BCI.}, }
@article {pmid37007206, year = {2023}, author = {Li, MA and Ruan, ZW}, title = {Decoding motor imagery with a simplified distributed dipoles model at source level.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {2}, pages = {445-457}, pmid = {37007206}, issn = {1871-4080}, abstract = {Motor imagery (MI) based brain computer interface significantly oriented the development of neuro-rehabilitation, and the crucial issue is how to accurately detect the changes of cerebral cortex for MI decoding. The brain activity can be calculated based on the head model and observed scalp EEG, providing insights regarding cortical dynamics by using equivalent current dipoles with high spatial and temporal resolution. Now, all the dipoles within entire cortex or partial regions of interest are directly applied to data representation, this may make the key information weakened or lost, and it is worth studying how to choose the most important from numerous dipoles. In this paper, we devote to building a simplified distributed dipoles model (SDDM), which is combined with convolutional neural network (CNN), generating a MI decoding method at source level (called SDDM-CNN). First, all channels of raw MI-EEG signals are subdivided by a series of bandpass filters with width of 1 Hz, the average energies associated with any sub-band signals are calculated and ranked in a descending order to screen the top n sub-bands; then, the MI-EEG signals over each selected sub-band are mapped into source space by using EEG source imaging technology, and for each scout of neuroanatomical Desikan-Killiany partition, a centered dipole is selected as the most relevant dipole and put together to build a SDDM to reflect the neuroelectric activity of entire cerebral cortex; finally, the 4 dimensional (4D) magnitude matrix is constructed for each SDDM and fused into a novel data representation, which is further input to a well-designed 3DCNN with n parallel branches (nB3DCNN) to extract and classify the comprehensive features from time-frequency-space dimensions. Experiments are carried out on three public datasets, and the average ten-fold CV decoding accuracies achieve 95.09%, 97.98% and 94.53% respectively, and the statistical analysis is fulfilled by standard deviation, kappa value and confusion matrix. Experiment results suggest that it is beneficial to pick out the most sensitive sub-bands in sensor domain, and SDDM can sufficiently describe the dynamic changing of entire cortex, improving decoding performance while greatly reducing number of source signals. Also, nB3DCNN is capable of exploring spatial-temporal features from multi sub-bands.}, }
@article {pmid37007202, year = {2023}, author = {Pan, H and Li, Z and Tian, C and Wang, L and Fu, Y and Qin, X and Liu, F}, title = {The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {2}, pages = {373-384}, pmid = {37007202}, issn = {1871-4080}, abstract = {Brain-computer interface (BCI) can obtain text information by decoding language induced electroencephalogram (EEG) signals, so as to restore communication ability for patients with language impairment. At present, the BCI system based on speech imagery of Chinese characters has the problem of low accuracy of features classification. In this paper, the light gradient boosting machine (LightGBM) is adopted to recognize Chinese characters and solve the above problems. Firstly, the Db4 wavelet basis function is selected to decompose the EEG signals in six-layer of full frequency band, and the correlation features of Chinese characters speech imagery with high time resolution and high frequency resolution are extracted. Secondly, the two core algorithms of LightGBM, gradient-based one-side sampling and exclusive feature bundling, are used to classify the extracted features. Finally, we verify that classification performance of LightGBM is more accurate and applicable than the traditional classifiers according to the statistical analysis methods. We evaluate the proposed method through contrast experiment. The experimental results show that the average classification accuracy of the subjects' silent reading of Chinese characters "(left)", "(one)" and simultaneous silent reading is improved by 5.24%, 4.90% and 12.44% respectively.}, }
@article {pmid37007196, year = {2023}, author = {Martín-Chinea, K and Ortega, J and Gómez-González, JF and Pereda, E and Toledo, J and Acosta, L}, title = {Effect of time windows in LSTM networks for EEG-based BCIs.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {2}, pages = {385-398}, pmid = {37007196}, issn = {1871-4080}, abstract = {People with impaired motor function could be helped by an effective brain-computer interface (BCI) based on a real-time electroencephalogram (EEG) and artificial intelligence algorithms. However, current methodologies for interpreting patient instructions from an EEG are not accurate enough to be completely safe in a real-world situation , where a poor decision would place their physical integrity at risk, such as when traveling in an electric wheelchair in a city. For various reasons, such as the low signal-to-noise ratio of portable EEGs or the effects of signal contamination (disturbances due to user movement, temporal variation of the features of EEG signals, etc.), a long short-term memory network (LSTM) (a type of recurrent neural network) that is able to learn data flow patterns from EEG signals could improve the classification of the actions taken by the user. In this paper, the effectiveness of using an LSTM with a low-cost wireless EEG device in real time is tested, and the time window that maximizes its classification accuracy is studied. The goal is to be able to implement it in the BCI of a smart wheelchair with a simple coded command protocol, such as opening or closing the eyes, which could be executed by patients with reduced mobility. Results show a higher resolution of the LSTM with an accuracy range between 77.61 and 92.14% compared to traditional classifiers (59.71%), and an optimal time window of around 7 s for the task done by users in this work. In addition, tests in real-life contexts show that a trade-off between accuracy and response times is necessary to ensure detection.}, }
@article {pmid37007193, year = {2023}, author = {Chen, M and Zhu, Y and Zhang, R and Yu, R and Hu, Y and Wan, H and Yao, D and Guo, D}, title = {A model description of beta oscillations in the external globus pallidus.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {2}, pages = {477-487}, pmid = {37007193}, issn = {1871-4080}, abstract = {The external globus pallidus (GPe), a subcortical nucleus located in the indirect pathway of the basal ganglia, is widely considered to have tight associations with abnormal beta oscillations (13-30 Hz) observed in Parkinson's disease (PD). Despite that many mechanisms have been put forward to explain the emergence of these beta oscillations, however, it is still unclear the functional contributions of the GPe, especially, whether the GPe itself can generate beta oscillations. To investigate the role played by the GPe in producing beta oscillations, we employ a well described firing rate model of the GPe neural population. Through extensive simulations, we find that the transmission delay within the GPe-GPe pathway contributes significantly to inducing beta oscillations, and the impacts of the time constant and connection strength of the GPe-GPe pathway on generating beta oscillations are non-negligible. Moreover, the GPe firing patterns can be significantly modulated by the time constant and connection strength of the GPe-GPe pathway, as well as the transmission delay within the GPe-GPe pathway. Interestingly, both increasing and decreasing the transmission delay can push the GPe firing pattern from beta oscillations to other firing patterns, including oscillation and non-oscillation firing patterns. These findings suggest that if the transmission delays within the GPe are at least 9.8 ms, beta oscillations can be produced originally in the GPe neural population, which also may be the origin of PD-related beta oscillations and should be regarded as a promising target for treatments for PD.}, }
@article {pmid37003976, year = {2023}, author = {Liu, X and Zhang, W and Li, W and Zhang, S and Lv, P and Yin, Y}, title = {Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial.}, journal = {BMC neurology}, volume = {23}, number = {1}, pages = {136}, pmid = {37003976}, issn = {1471-2377}, abstract = {BACKGROUND: Seeking positive and comprehensive rehabilitation methods after stroke is an urgent problem to be solved, which is very important to improve the dysfunction of stroke. The aim of this study was to investigate the effects of motor imagery-based brain-computer interface training (MI-BCI) on upper limb function and attention in stroke patients with hemiplegia.<br><br>METHODS: Sixty stroke patients with impairment of upper extremity function and decreased attention were randomly assigned to the control group (CR group) or the experimental group (BCI group) in a 1:1 ratio. Patients in the CR group received conventional rehabilitation. Patients in the BCI group received 20&#xa0;min of MI-BCI training five times a week for 3 weeks (15 sessions) in addition to conventional rehabilitation. The primary outcome measures were the changes in Fugl-Meyer Motor Function Assessment of Upper Extremities (FMA-UE) and Attention Network Test (ANT) from baseline to 3 weeks.<br><br>RESULTS: About 93% of the patients completed the allocated training. Compared with the CR group, among those in the BCI group, FMA-UE was increased by 8.0 points (95%CI, 5.0 to 10.0; P&#x2009;<&#x2009;0.001). Alert network response time (32.4ms; 95%CI, 58.4 to 85.6; P&#x2009;<&#x2009;0.001), orienting network response (5.6ms; 95%CI, 29.8 to 55.8; P&#x2009;=&#x2009;0.010), and corrects number (8.0; 95%CI, 17.0 to 28.0; P&#x2009;<&#x2009;0.001) also increased in the BCI group compared with the CR group. Additionally, the executive control network response time (-&#x2009;105.9ms; 95%CI, -&#x2009;68.3 to -&#x2009;23.6; P&#x2009;=&#x2009;0.002), the total average response time (-&#x2009;244.8ms; 95%CI, -&#x2009;155.8 to -&#x2009;66.2; P&#x2009;=&#x2009;0.002), and total time (-&#x2009;122.0ms; 95%CI, -&#x2009;80.0 to -&#x2009;35.0; P&#x2009;=&#x2009;0.001) were reduced in the BCI group compared with the CR group.<br><br>CONCLUSION: MI-BCI combined with conventional rehabilitation training could better enhance upper limb motor function and attention in stroke patients. This training method may be feasible and suitable for individuals with stroke.<br><br>TRIAL REGISTRATION: This study was registered in the Chinese Clinical Trial Registry with Portal Number ChiCTR2100050430(27/08/2021).}, }
@article {pmid37002922, year = {2023}, author = {Rosenthal, IA and Bashford, L and Kellis, S and Pejsa, K and Lee, B and Liu, C and Andersen, RA}, title = {S1 represents multisensory contexts and somatotopic locations within and outside the bounds of the cortical homunculus.}, journal = {Cell reports}, volume = {42}, number = {4}, pages = {112312}, doi = {10.1016/j.celrep.2023.112312}, pmid = {37002922}, issn = {2211-1247}, abstract = {Recent literature suggests that tactile events are represented in the primary somatosensory cortex (S1) beyond its long-established topography; in addition, the extent to which S1 is modulated by vision remains unclear. To better characterize S1, human electrophysiological data were recorded during touches to the forearm or finger. Conditions included visually observed physical touches, physical touches without vision, and visual touches without physical contact. Two major findings emerge from this dataset. First, vision strongly modulates S1 area 1, but only if there is a physical element to the touch, suggesting that passive touch observation is insufficient to elicit neural responses. Second, despite recording in a putative arm area of S1, neural activity represents both arm and finger stimuli during physical touches. Arm touches are encoded more strongly and specifically, supporting the idea that S1 encodes tactile events primarily through its topographic organization but also more generally, encompassing other areas of the body.}, }
@article {pmid37001511, year = {2023}, author = {Mahapatra, NC and Bhuyan, P}, title = {EEG-based classification of imagined digits using recurrent neural network.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acc976}, pmid = {37001511}, issn = {1741-2552}, abstract = {OBJECTIVE: In recent years, imagined speech brain-computer (machine) interface applications have been an important field of study that can improve the lives of patients with speech problems through alternative verbal communication. This study aimed to classify the imagined speech of numerical digits from electroencephalography (EEG) signals by exploiting the past and future temporal characteristics of the signal using several deep learning models.<br><br>APPROACH: This study proposed a methodological combination of EEG signal processing techniques and deep learning models for the recognition of imagined speech signals. The EEG signals were filtered and preprocessed using the discrete wavelet transformation (DWT) to remove artifacts and retrieve feature information. To classify the preprocessed imagined speech neural signals, multiple versions of multlayer bidirectional recurrent neural networks were used.<br><br>MAIN RESULTS: The method was examined by leveraging MUSE and EPOC signals from MNIST imagined digits in the MindBigData open-access database. The presented methodology's classification performance accuracy was noteworthy, with the model's multiclass overall classification accuracy reaching a maximum of 96.18 percent on MUSE signals and 71.60 percent on EPOC signals.<br><br>SIGNIFICANCE: Furthermore, this research shows that the proposed signal preprocessing approach and the stacked bidirectional recurrent network model are suitable for extracting the high temporal resolution of EEG signals in order to classify imagined digits, indicating the unique neural identity of each imagined digit class that distinguished it from the others.}, }
@article {pmid37001500, year = {2023}, author = {Li, HY and Zhu, MZ and Yuan, XR and Guo, ZX and Pan, YD and Li, YQ and Zhu, XH}, title = {A thalamic-primary auditory cortex circuit mediates resilience to stress.}, journal = {Cell}, volume = {186}, number = {7}, pages = {1352-1368.e18}, doi = {10.1016/j.cell.2023.02.036}, pmid = {37001500}, issn = {1097-4172}, abstract = {Resilience enables mental elasticity in individuals when rebounding from adversity. In this study, we identified a microcircuit and relevant molecular adaptations that play a role in natural resilience. We found that activation of parvalbumin (PV) interneurons in the primary auditory cortex (A1) by thalamic inputs from the ipsilateral medial geniculate body (MG) is essential for resilience in mice exposed to chronic social defeat stress. Early attacks during chronic social defeat stress induced short-term hyperpolarizations of MG neurons projecting to the A1 (MG[A1] neurons) in resilient mice. In addition, this temporal neural plasticity of MG[A1] neurons initiated synaptogenesis onto thalamic PV neurons via presynaptic BDNF-TrkB signaling in subsequent stress responses. Moreover, optogenetic mimicking of the short-term hyperpolarization of MG[A1] neurons, rather than merely activating MG[A1] neurons, elicited innate resilience mechanisms in response to stress and achieved sustained antidepressant-like effects in multiple animal models, representing a new strategy for targeted neuromodulation.}, }
@article {pmid37000276, year = {2023}, author = {Willenborg, K and Lenarz, T and Busch, S}, title = {Surgical and audiological outcomes with a new transcutaneous bone conduction device with reduced transducer thickness in children.}, journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery}, volume = {}, number = {}, pages = {}, pmid = {37000276}, issn = {1434-4726}, abstract = {PURPOSE: Due to smaller bone thickness, young children with conductive or mixed hearing loss or single-sided deafness were previously most commonly treated with a percutaneous osseointegrated bone-anchored hearing aid (BAHA) or an active middle-ear implant. While the BAHA increases the risk of implant infections, skin infection, overgrowth of the screw or involvement of the implant in head trauma, middle-ear implant surgery involves manipulation of the ossicles with possible risk of surgical trauma. These complications can be omitted with transcutaneous bone conduction implant systems like the MED-EL Bonebridge system. The purpose of this study was to analyze whether the second generation of the Bonebridge (BCI 602) that features a decreased implant thickness with a reduced surgical drilling depth can be implanted safely in young children with good postoperative hearing performance.<br><br>METHODS: In this study, 14 patients under 12&#xa0;years were implanted with the second generation of the Bonebridge. Preoperative workup comprised a CT scan, an MRI scan, pure tone audiometry, or alternatively a BERA (bone conduction, air conduction). Since children under 12&#xa0;years often have a lower bone thickness, the CT was performed to determine the suitability of the temporal bone for optimal implant placement using the Otoplan software.<br><br>RESULTS: All patients (including three under the age of five) were successfully implanted and showed a good postoperative hearing performance.<br><br>CONCLUSION: With adequate preoperative workup, this device can be safely implanted in children and even children under 5&#xa0;years of age and allows for an extension of indication criteria toward younger children.}, }
@article {pmid36999132, year = {2023}, author = {Liao, W and Li, J and Zhang, X and Li, C}, title = {Motor imagery brain-computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1117670}, pmid = {36999132}, issn = {1662-5161}, abstract = {This study compared the efficacy of Motor Imagery brain-computer interface (MI-BCI) combined with physiotherapy and physiotherapy alone in ischemic stroke before and after rehabilitation training. We wanted to explore whether the rehabilitation effect of MI-BCI is affected by the severity of the patient's condition and whether MI-BCI was effective for all patients. Forty hospitalized patients with ischemic stroke with motor deficits participated in this study. The patients were divided into MI and control groups. Functional assessments were performed before and after rehabilitation training. The Fugl-Meyer Assessment (FMA) was used as the primary outcome measure, and its shoulder and elbow scores and wrist scores served as secondary outcome measures. The motor assessment scale (MAS) was used to assess motor function recovery. We used non-contrast CT (NCCT) to investigate the influence of different types of middle cerebral artery high-density signs on the prognosis of ischemic stroke. Brain topographic maps can directly reflect the neural activity of the brain, so we used them to detect changes in brain function and brain topological power response after stroke. Compared the MI group and control group after rehabilitation training, better functional outcome was observed after MI-BCI rehabilitation, including a significantly higher probability of achieving a relevant increase in the Total FMA scores (MI = 16.70 ± 12.79, control = 5.34 ± 10.48), FMA shoulder and elbow scores (MI = 12.56 ± 6.37, control = 2.45 ± 7.91), FMA wrist scores (MI = 11.01 ± 3.48, control = 3.36 ± 5.79), the MAS scores (MI = 3.62 ± 2.48, control = 1.85 ± 2.89), the NCCT (MI = 21.94 ± 2.37, control = 17.86 ± 3.55). The findings demonstrate that MI-BCI rehabilitation training could more effectively improve motor function after upper limb motor dysfunction after stroke compared with routine rehabilitation training, which verifies the feasibility of active induction of neural rehabilitation. The severity of the patient's condition may affect the rehabilitation effect of the MI-BCI system.}, }
@article {pmid36998726, year = {2023}, author = {Quiles, V and Ferrero, L and Iáñez, E and Ortiz, M and Gil-Agudo, &#xc1; and Azorín, JM}, title = {Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1154480}, pmid = {36998726}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challenges that classical processing techniques do not solve, showing certain shortcomings in the real-time domain. Recent advances in deep-learning techniques open a window of opportunity to solve some of these problems. In this work, an interface able to detect the evoked potential that occurs when a person intends to stop due to the appearance of an unexpected obstacle has been developed.<br><br>MATERIAL AND METHODS: First, the interface was tested on a treadmill with five subjects, in which the user stopped when an obstacle appeared (simulated by a laser). The analysis is based on two consecutive convolutional networks: the first one to discern the intention to stop against normal walking and the second one to correct false detections of the previous one.<br><br>RESULTS AND DISCUSSION: The results were superior when using the methodology of the two consecutive networks vs. only the first one in a cross-validation pseudo-online analysis. The false positives per min (FP/min) decreased from 31.8 to 3.9 FP/min and the number of repetitions in which there were no false positives and true positives (TP) improved from 34.9% to 60.3% NOFP/TP. This methodology was tested in a closed-loop experiment with an exoskeleton, in which the brain-machine interface (BMI) detected an obstacle and sent the command to the exoskeleton to stop. This methodology was tested with three healthy subjects, and the online results were 3.8 FP/min and 49.3% NOFP/TP. To make this model feasible for non-able bodied patients with a reduced and manageable time frame, transfer-learning techniques were applied and validated in the previous tests, and were then applied to patients. The results for two incomplete Spinal Cord Injury (iSCI) patients were 37.9% NOFP/TP and 7.7 FP/min.}, }
@article {pmid36997155, year = {2023}, author = {Wang, DX and Ng, N and Seger, SE and Ekstrom, AD and Kriegel, JL and Lega, BC}, title = {Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad105}, pmid = {36997155}, issn = {1460-2199}, support = {R01NS125250/NH/NIH HHS/United States ; }, abstract = {Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode implantation locations. Using a data-driven approach, we employ support vector machine (SVM) classifiers to identify high-yield brain targets on a large data set of 75 human intracranial electroencephalogram subjects performing the free recall (FR) task. Further, we address whether the conserved brain regions provide effective classification in an alternate (associative) memory paradigm along with FR, as well as testing unsupervised classification methods that may be a useful adjunct to clinical device implementation. Finally, we use random forest models to classify functional brain states, differentiating encoding versus retrieval versus non-memory behavior such as rest and mathematical processing. We then test how regions that exhibit good classification for the likelihood of recall success in the SVM models overlap with regions that differentiate functional brain states in the random forest models. Finally, we lay out how these data may be used in the design of neuromodulation devices.}, }
@article {pmid36993576, year = {2023}, author = {Tou, SLJ and Warschausky, SA and Karlsson, P and Huggins, JE}, title = {Individualized Electrode Subset Improves the Calibration Accuracy of an EEG P300-design Brain-Computer Interface for People with Severe Cerebral Palsy.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.03.22.533775}, pmid = {36993576}, abstract = {OBJECTIVE: This study examined the effect of individualized electroencephalogram (EEG) electrode location selection for non-invasive P300-design brain-computer interfaces (BCIs) in people with varying severity of cerebral palsy (CP).<br><br>APPROACH: A forward selection algorithm was used to select the best performing 8 electrodes (of an available 32) to construct an individualized electrode subset for each participant. BCI accuracy of the individualized subset was compared to accuracy of a widely used default subset.<br><br>MAIN RESULTS: Electrode selection significantly improved BCI calibration accuracy for the group with severe CP. Significant group effect was not found for the group of typically developing controls and the group with mild CP. However, several individuals with mild CP showed improved performance. Using the individualized electrode subsets, there was no significant difference in accuracy between calibration and evaluation data in the mild CP group, but there was a reduction in accuracy from calibration to evaluation in controls.<br><br>SIGNIFICANCE: The findings suggested that electrode selection can accommodate developmental neurological impairments in people with severe CP, while the default electrode locations are sufficient for many people with milder impairments from CP and typically developing individuals.}, }
@article {pmid36991884, year = {2023}, author = {Hashem, HA and Abdulazeem, Y and Labib, LM and Elhosseini, MA and Shehata, M}, title = {An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {6}, pages = {}, doi = {10.3390/s23063171}, pmid = {36991884}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Quality of Life ; Electroencephalography/methods ; Algorithms ; Machine Learning ; }, abstract = {Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model's predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.}, }
@article {pmid36988341, year = {2023}, author = {Nie, A and Guo, B}, title = {Benefits and Detriments of Social Collaborative Memory in Turn-Taking and Directed Forgetting.}, journal = {Perceptual and motor skills}, volume = {}, number = {}, pages = {315125231163626}, doi = {10.1177/00315125231163626}, pmid = {36988341}, issn = {1558-688X}, abstract = {Collaborative recall by groups of people can evoke both memory detriments (e.g., collaborative inhibition) and benefits (e.g., error pruning and post-collaborative memory benefit). Yet, it remains indeterminate whether these effects are due to the emotional valence of stimuli and/or the specific subtypes of episodic memory tested (i.e., item memory and source memory), and whether they are related to the research procedure of directed forgetting (DF). We introduced item-method DF into collaborative memory research using a turn-taking procedure. The to-be-recalled words were studied in different emotional valences and were followed by either an R or F cue, which, respectively, instructed participants to remember or forget the words presented. We conducted two recall sessions (Recall 1 and Recall 2) that included the two subtypes of episodic memory. Recall 1 was performed either individually or collaboratively, while Recall 2 was always performed individually. We observed three major findings: (a) a collaborative memory decrement - collaborative inhibition - was minimally affected in both item memory and source memory tasks by either the emotional valence of the stimuli or the DF cue; (b) a collaborative memory benefit - error pruning of item memory - persisted within both ongoing and post-collaboration, while error pruning of source memory only presented in ongoing collaboration, thus demonstrating the relevance of dual-process models that differentiate automatic familiarity and effortful recollection processes; and (c) there was no post-collaborative memory benefit, indicating the importance of the type of collaborative procedure. We discuss these results in terms of various theories, including the retrieval strategy disruption hypothesis (RSDH) which asserts that memory strategies tend to be disrupted in collaboration but are facilitated within post-collaboration. Also, we describe the implications of these results and directions for exploring other influential factors in future research.}, }
@article {pmid36987698, year = {2023}, author = {Youssofzadeh, V and Roy, S and Chowdhury, A and Izadysadr, A and Parkkonen, L and Raghavan, M and Prasad, G}, title = {Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG.}, journal = {Human brain mapping}, volume = {}, number = {}, pages = {}, doi = {10.1002/hbm.26284}, pmid = {36987698}, issn = {1097-0193}, abstract = {Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain-computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17-25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke.}, }
@article {pmid36985341, year = {2023}, author = {Li, A and He, Y and Yang, C and Lu, N and Bao, J and Gao, S and Hosyanto, FF and He, X and Fu, H and Yan, H and Ding, N and Xu, L}, title = {Methylprednisolone Promotes Mycobacterium smegmatis Survival in Macrophages through NF-κB/DUSP1 Pathway.}, journal = {Microorganisms}, volume = {11}, number = {3}, pages = {}, pmid = {36985341}, issn = {2076-2607}, abstract = {BACKGROUND: Mycobacterium tuberculosis (M. tuberculosis) is the causative agent of tuberculosis. As an important component of host immunity, macrophages are not only the first line of defense against M. tuberculosis but also the parasitic site of M. tuberculosis in the host. Glucocorticoids can cause immunosuppression, which is considered to be one of the major risk factors for active tuberculosis, but the mechanism is unclear.<br><br>OBJECTIVE: To study the effect of methylprednisolone on the proliferation of mycobacteria in macrophages and try to find key molecules of this phenomenon.<br><br>METHODS: The macrophage line RAW264.7 infected by M. smegmatis was treated with methylprednisolone, and the intracellular bacterial CFU, Reactive Oxygen Species (ROS), cytokine secretion, autophagy, and apoptosis were measured. After the cells were treated with NF-&#x3ba;B inhibitor BAY 11-7082 and DUSP1 inhibitor BCI, respectively, the intracellular bacterial CFU, ROS, IL-6, and TNF-&#x3b1; secretion were detected.<br><br>RESULTS: After treatment with methylprednisolone, the CFU of intracellular bacteria increased, the level of ROS decreased, and the secretion of IL-6 and TNF-&#x3b1; decreased in infected macrophages. After BAY 11-7082 treatment, the CFU of M. smegmatis in macrophages increased, and the level of ROS production and the secretion of IL-6 by macrophages decreased. Transcriptome high-throughput sequencing and bioinformatics analysis suggested that DUSP1 was the key molecule in the above phenomenon. Western blot analysis confirmed that the expression level of DUSP1 was increased in the infected macrophages treated with methylprednisolone and BAY 11-7082, respectively. After BCI treatment, the level of ROS produced by infected macrophages increased, and the secretion of IL-6 increased. After the treatment of BCI combined with methylprednisolone or BAY 11-7082, the level of ROS produced and the secretion of IL-6 by macrophages were increased.<br><br>CONCLUSION: methylprednisolone promotes the proliferation of mycobacteria in macrophages by suppressing cellular ROS production and IL-6 secretion through down-regulating NF-&#x3ba;B and up-regulating DUSP1 expression. BCI, an inhibitor of DUSP1, can reduce the level of DUSP1 in the infected macrophages and inhibit the proliferation of intracellular mycobacteria by promoting cellular ROS production and IL-6 secretion. Therefore, BCI may become a new molecule for host-directed therapy of tuberculosis, as well as a new strategy for the prevention of tuberculosis when treated with glucocorticoids.}, }
@article {pmid36985087, year = {2023}, author = {Jeakle, EN and Abbott, JR and Usoro, JO and Wu, Y and Haghighi, P and Radhakrishna, R and Sturgill, BS and Nakajima, S and Thai, TTD and Pancrazio, JJ and Cogan, SF and Hernandez-Reynoso, AG}, title = {Chronic Stability of Local Field Potentials Using Amorphous Silicon Carbide Microelectrode Arrays Implanted in the Rat Motor Cortex.}, journal = {Micromachines}, volume = {14}, number = {3}, pages = {}, pmid = {36985087}, issn = {2072-666X}, support = {R01NS104344/NH/NIH HHS/United States ; }, abstract = {Implantable microelectrode arrays (MEAs) enable the recording of electrical activity of cortical neurons, allowing the development of brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic conditions, prompting the development of novel MEAs that can improve long-term performance. Conventional planar, silicon-based devices and ultra-thin amorphous silicon carbide (a-SiC) MEAs were implanted in the motor cortex of female Sprague-Dawley rats, and weekly anesthetized recordings were made for 16 weeks after implantation. The spectral density and bandpower between 1 and 500 Hz of recordings were compared over the implantation period for both device types. Initially, the bandpower of the a-SiC devices and standard MEAs was comparable. However, the standard MEAs showed a consistent decline in both bandpower and power spectral density throughout the 16 weeks post-implantation, whereas the a-SiC MEAs showed substantially more stable performance. These differences in bandpower and spectral density between standard and a-SiC MEAs were statistically significant from week 6 post-implantation until the end of the study at 16 weeks. These results support the use of ultra-thin a-SiC MEAs to develop chronic, reliable brain-machine interfaces.}, }
@article {pmid36982255, year = {2023}, author = {Li, J and Cheng, Y and Gu, M and Yang, Z and Zhan, L and Du, Z}, title = {Sensing and Stimulation Applications of Carbon Nanomaterials in Implantable Brain-Computer Interface.}, journal = {International journal of molecular sciences}, volume = {24}, number = {6}, pages = {}, doi = {10.3390/ijms24065182}, pmid = {36982255}, issn = {1422-0067}, abstract = {Implantable brain-computer interfaces (BCIs) are crucial tools for translating basic neuroscience concepts into clinical disease diagnosis and therapy. Among the various components of the technological chain that increases the sensing and stimulation functions of implanted BCI, the interface materials play a critical role. Carbon nanomaterials, with their superior electrical, structural, chemical, and biological capabilities, have become increasingly popular in this field. They have contributed significantly to advancing BCIs by improving the sensor signal quality of electrical and chemical signals, enhancing the impedance and stability of stimulating electrodes, and precisely modulating neural function or inhibiting inflammatory responses through drug release. This comprehensive review provides an overview of carbon nanomaterials' contributions to the field of BCI and discusses their potential applications. The topic is broadened to include the use of such materials in the field of bioelectronic interfaces, as well as the potential challenges that may arise in future implantable BCI research and development. By exploring these issues, this review aims to provide insight into the exciting developments and opportunities that lie ahead in this rapidly evolving field.}, }
@article {pmid36981352, year = {2023}, author = {Sheng, J and Xu, J and Li, H and Liu, Z and Zhou, H and You, Y and Song, T and Zuo, G}, title = {A Multi-Scale Temporal Convolutional Network with Attention Mechanism for Force Level Classification during Motor Imagery of Unilateral Upper-Limb Movements.}, journal = {Entropy (Basel, Switzerland)}, volume = {25}, number = {3}, pages = {}, doi = {10.3390/e25030464}, pmid = {36981352}, issn = {1099-4300}, abstract = {In motor imagery (MI) brain-computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient's demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain's force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time-frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time-frequency-space domain features to improve the algorithm's sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance.}, }
@article {pmid36980430, year = {2023}, author = {García-Murillo, DG and Álvarez-Meza, AM and Castellanos-Dominguez, CG}, title = {KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {13}, number = {6}, pages = {}, doi = {10.3390/diagnostics13061122}, pmid = {36980430}, issn = {2075-4418}, abstract = {This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject's unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain-computer interface systems.}, }
@article {pmid36979295, year = {2023}, author = {Liu, Y and Zhang, Y and Jiang, Z and Kong, W and Zou, L}, title = {Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation.}, journal = {Brain sciences}, volume = {13}, number = {3}, pages = {}, doi = {10.3390/brainsci13030485}, pmid = {36979295}, issn = {2076-3425}, abstract = {BACKGROUND: It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) study in a gambling task by utilizing tensor decomposition.<br><br>METHODS: First, the single-subject EEG data are represented as a third-order spectrogram tensor to extract frequency features. Next, the EEG and fMRI data are jointly decomposed into a superposition of multiple sources characterized by space-time-frequency profiles using coupled matrix tensor factorization (CMTF). Finally, graph-structured clustering is used to select the most appropriate model according to four quantitative indices.<br><br>RESULTS: The results clearly show that not only are the regions of interest (ROIs) found in other literature activated, but also the olfactory cortex and fusiform gyrus which are usually ignored. It is found that regions including the orbitofrontal cortex and insula are activated for both winning and losing stimuli. Meanwhile, regions such as the superior orbital frontal gyrus and anterior cingulate cortex are activated upon winning stimuli, whereas the inferior frontal gyrus, cingulate cortex, and medial superior frontal gyrus are activated upon losing stimuli.<br><br>CONCLUSION: This work sheds light on the reward-processing progress, provides a deeper understanding of brain function, and opens a new avenue in the investigation of neurovascular coupling via CMTF.}, }
@article {pmid36979293, year = {2023}, author = {Xu, D and Tang, F and Li, Y and Zhang, Q and Feng, X}, title = {An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.}, journal = {Brain sciences}, volume = {13}, number = {3}, pages = {}, doi = {10.3390/brainsci13030483}, pmid = {36979293}, issn = {2076-3425}, abstract = {The brain-computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011-2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.}, }
@article {pmid36978672, year = {2023}, author = {Wang, T and Chen, YH and Sawan, M}, title = {Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {3}, pages = {}, doi = {10.3390/bioengineering10030281}, pmid = {36978672}, issn = {2306-5354}, abstract = {Motor imagery-based brain-computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.}, }
@article {pmid36972585, year = {2023}, author = {Zhang, T and Rahimi Azghadi, M and Lammie, C and Amirsoleimani, A and Genov, R}, title = {Spike sorting algorithms and their efficient hardware implementation: A comprehensive survey.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acc7cc}, pmid = {36972585}, issn = {1741-2552}, abstract = {Spike sorting is a set of techniques used to analyze extracellular neural recordings, attributing individual spikes to individual neurons. This field has gained significant interest in neuroscience due to advances in implantable microelectrode arrays, capable of recording thousands of neurons simultaneously. High-density electrodes, combined with efficient and accurate spike sorting systems, are essential for various applications, including Brain Machine Interfaces (BMI), experimental neural prosthetics, real-time neurological disorder monitoring, and neuroscience research. However, given the resource constraints of modern applications, relying solely on algorithmic innovation is not enough. Instead, a co-optimization approach that combines hardware and spike sorting algorithms must be taken to develop neural recording systems suitable for resource-constrained environments, such as wearable devices and BMIs. This co-design requires careful consideration when selecting appropriate spike-sorting algorithms that match specific hardware and use cases. Approach: We investigated the recent literature on spike sorting, both in terms of hardware advancements and algorithms innovations. Moreover, we dedicated special attention to identifying suitable algorithm-hardware combinations, and their respective real-world applicabilities. Main Results: In this review, we first examined the current progress in algorithms, and described the recent departure from the conventional "3- step" algorithms in favor of more advanced template matching or machine-learning-based techniques. Next, we explored innovative hardware options, including Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), and In-Memory Computing Devices (IMCs). Additionally, the challenges and future opportunities for spike sorting are discussed. Significance: This comprehensive review systematically summarizes the latest spike sorting techniques and demonstrates how they enable researchers to overcome traditional obstacles and unlock novel applications. Our goal is for this work to serve as a roadmap for future researchers seeking to identify the most appropriate spike sorting implementations for various experimental settings. By doing so, we aim to facilitate the advancement of this exciting field and promote the development of innovative solutions that drive progress in neural engineering research.}, }
@article {pmid36968496, year = {2023}, author = {Rueckauer, B and van Gerven, M}, title = {An in-silico framework for modeling optimal control of neural systems.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1141884}, pmid = {36968496}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems. This work proposes a scalable, data-driven, unified approach to study brain-machine-environment interaction using established tools from dynamical systems, optimal control theory, and deep learning.<br><br>METHODS: To unify the methodology, we define the environment, neural system, and prosthesis in terms of differential equations with learnable parameters, which effectively reduce to recurrent neural networks in the discrete-time case. Drawing on tools from optimal control, we describe three ways to train the system: Direct optimization of an objective function, oracle-based learning, and reinforcement learning. These approaches are adapted to different assumptions about knowledge of system equations, linearity, differentiability, and observability.<br><br>RESULTS: We apply the proposed framework to train an in-silico neural system to perform tasks in a linear and a nonlinear environment, namely particle stabilization and pole balancing. After training, this model is perturbed to simulate impairment of sensor and motor function. We show how a prosthetic controller can be trained to restore the behavior of the neural system under increasing levels of perturbation.<br><br>DISCUSSION: We expect that the proposed framework will enable rapid and flexible synthesis of control algorithms for neural prostheses that reduce the need for in-vivo testing. We further highlight implications for sparse placement of prosthetic sensor and actuator components.}, }
@article {pmid36968493, year = {2023}, author = {Hu, YT and Chen, XL and Zhang, YN and McGurran, H and Stormmesand, J and Breeuwsma, N and Sluiter, A and Zhao, J and Swaab, D and Bao, AM}, title = {Sex differences in hippocampal β-amyloid accumulation in the triple-transgenic mouse model of Alzheimer's disease and the potential role of local estrogens.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1117584}, pmid = {36968493}, issn = {1662-4548}, abstract = {INTRODUCTION: Epidemiological studies show that women have a higher prevalence of Alzheimer's disease (AD) than men. Peripheral estrogen reduction during aging in women is proposed to play a key role in this sex-associated prevalence, however, the underlying mechanism remains elusive. We previously found that transcription factor early growth response-1 (EGR1) significantly regulates cholinergic function. EGR1 stimulates acetylcholinesterase (AChE) gene expression and is involved in AD pathogenesis. We aimed to investigate whether the triple-transgenic AD (3xTg-AD) mice harboring PS1 [M146V] , APP [Swe] , and Tau [P301L] show sex differences in β-amyloid (Aβ) and hyperphosphorylated tau (p-Tau), the two primary AD hallmarks, and how local 17β-estradiol (E2) may regulate the expression of EGR1 and AChE.<br><br>METHODS: We first sacrificed male and female 3xTg-AD mice at 3-4, 7-8, and 11-12 months and measured the levels of A&#x3b2;, p-Tau, EGR1, and AChE in the hippocampal complex. Second, we infected SH-SY5Y cells with lentivirus containing the amyloid precursor protein construct C99, cultured with or without E2 administration we measured the levels of extracellular A&#x3b2; and intracellular EGR1 and AChE.<br><br>RESULTS: Female 3xTg-AD mice had higher levels of A&#x3b2; compared to males, while no p-Tau was found in either group. In SH-SY5Y cells infected with lentivirus containing the amyloid precursor protein construct C99, we observed significantly increased extracellular A&#x3b2; and decreased expression of intracellular EGR1 and AChE. By adding E2 to the culture medium, extracellular A&#x3b2;(l-42) was significantly decreased while intracellular EGR1 and AChE expression were elevated.<br><br>DISCUSSION: This data shows that the 3xTg-AD mouse model can be useful for studying the human sex differences of AD, but only in regards to Ap. Furthermore, in vitro data shows local E2 may be protective for EGR1 and cholinergic functions in AD while suppressing soluble A&#x3b2;(1-42) levels. Altogether, this study provides further in vivo and in vitro data supporting the human epidemiological data indicating a higher prevalence of AD in women is related to changes in brain estrogen levels.}, }
@article {pmid36967197, year = {2023}, author = {Carpenter, KA and Thurlow, KE and Craig, SEL and Grainger, S}, title = {Wnt regulation of hematopoietic stem cell development and disease.}, journal = {Current topics in developmental biology}, volume = {153}, number = {}, pages = {255-279}, doi = {10.1016/bs.ctdb.2022.12.001}, pmid = {36967197}, issn = {1557-8933}, mesh = {*beta Catenin/metabolism ; *Wnt Proteins/metabolism ; Hematopoietic Stem Cells/metabolism ; Hematopoiesis ; Cell Differentiation/physiology ; Wnt Signaling Pathway ; }, abstract = {Hematopoietic stem cells (HSCs) are multipotent stem cells that give rise to all cells of the blood and most immune cells. Due to their capacity for unlimited self-renewal, long-term HSCs replenish the blood and immune cells of an organism throughout its life. HSC development, maintenance, and differentiation are all tightly regulated by cell signaling pathways, including the Wnt pathway. Wnt signaling is initiated extracellularly by secreted ligands which bind to cell surface receptors and give rise to several different downstream signaling cascades. These are classically categorized either β-catenin dependent (BCD) or β-catenin independent (BCI) signaling, depending on their reliance on the β-catenin transcriptional activator. HSC development, homeostasis, and differentiation is influenced by both BCD and BCI, with a high degree of sensitivity to the timing and dosage of Wnt signaling. Importantly, dysregulated Wnt signals can result in hematological malignancies such as leukemia, lymphoma, and myeloma. Here, we review how Wnt signaling impacts HSCs during development and in disease.}, }
@article {pmid36963740, year = {2023}, author = {Wu, H and Xie, Q and Pan, J and Liang, Q and Lan, Y and Guo, Y and Han, J and Xie, M and Liu, Y and Jiang, L and Wu, X and Li, Y and Qin, P}, title = {Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120050}, doi = {10.1016/j.neuroimage.2023.120050}, pmid = {36963740}, issn = {1095-9572}, abstract = {Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.}, }
@article {pmid36960685, year = {2023}, author = {Chen, XL and Fortes, JM and Hu, YT and van Iersel, J and He, KN and van Heerikhuize, J and Balesar, R and Swaab, D and Bao, AM}, title = {Sexually dimorphic age-related molecular differences in the entorhinal cortex of cognitively intact elderly: Relation to early Alzheimer's changes.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {}, number = {}, pages = {}, doi = {10.1002/alz.13037}, pmid = {36960685}, issn = {1552-5279}, abstract = {INTRODUCTION: Women are more vulnerable to Alzheimer's disease (AD) than men. The entorhinal cortex (EC) is one of the earliest structures affected in AD. We identified in cognitively intact elderly different molecular changes in the EC in relation to age.<br><br>METHODS: Changes in 12 characteristic molecules in relation to age were determined by quantitative immunohistochemistry or in situ hybridization in the EC. They were arbitrarily grouped into sex steroid-related molecules, markers of neuronal activity, neurotransmitter-related molecules, and cholinergic activity-related molecules.<br><br>RESULTS: The changes in molecules indicated increasing local estrogenic and neuronal activity accompanied by a higher and faster hyperphosphorylated tau accumulation in women's EC in relation to age, versus a mainly stable local estrogenic/androgenic and neuronal activity in men's EC.<br><br>DISCUSSION: EC employs a different neurobiological strategy in women and men to maintain cognitive function, which seems to be accompanied by an earlier start of AD in women.<br><br>HIGHLIGHTS: Local estrogen system is activated with age only in women's entorhinal cortex (EC). EC neuronal activity increased with age only in elderly women with intact cognition. Men and women have different molecular strategies to retain cognition with aging. P-tau accumulation in the EC was higher and faster in cognitively intact elderly women.}, }
@article {pmid36960172, year = {2023}, author = {Ma, Z and Wang, K and Xu, M and Yi, W and Xu, F and Ming, D}, title = {Transformed common spatial pattern for motor imagery-based brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1116721}, pmid = {36960172}, issn = {1662-4548}, abstract = {OBJECTIVE: The motor imagery (MI)-based brain-computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP.<br><br>APPROACH: This study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method.<br><br>MAIN RESULTS: As a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively.<br><br>SIGNIFICANCE: The results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs.}, }
@article {pmid36959601, year = {2023}, author = {Velasco, I and Sipols, A and De Blas, CS and Pastor, L and Bayona, S}, title = {Motor imagery EEG signal classification with a multivariate time series approach.}, journal = {Biomedical engineering online}, volume = {22}, number = {1}, pages = {29}, pmid = {36959601}, issn = {1475-925X}, abstract = {BACKGROUND: Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis RESULTS: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables.<br><br>CONCLUSIONS: This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.}, }
@article {pmid36951376, year = {2023}, author = {Saraswat, M and Dubey, AK}, title = {EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-22}, doi = {10.1080/10255842.2023.2187662}, pmid = {36951376}, issn = {1476-8259}, abstract = {Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain-computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.}, }
@article {pmid36950505, year = {2023}, author = {Fang, H and Yang, Y}, title = {Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1119685}, pmid = {36950505}, issn = {1662-5188}, abstract = {INTRODUCTION: Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.<br><br>METHODS: Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.<br><br>RESULTS: We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.<br><br>DISCUSSION: Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.}, }
@article {pmid36950147, year = {2023}, author = {Moly, A and Aksenov, A and Martel, F and Aksenova, T}, title = {Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square for epidural intracranial BCI.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1075666}, pmid = {36950147}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands.<br><br>METHODS: The use of sparse decoders may offer a way to address these challenges. A sparsity-promoting penalization is a common approach to obtaining a sparse solution. BCI features are naturally structured and grouped according to spatial (electrodes), frequency, and temporal dimensions. Applying group-wise sparsity, where the coefficients of a group are set to zero simultaneously, has the potential to decrease computational time and memory usage, as well as simplify data transfer. Additionally, online closed-loop decoder adaptation (CLDA) is known to be an efficient procedure for BCI decoder training, taking into account neuronal feedback. In this study, we propose a new algorithm for online closed-loop training of group-wise sparse multilinear decoders using L p -Penalized Recursive Exponentially Weighted N-way Partial Least Square (PREW-NPLS). Three types of sparsity-promoting penalization were explored using L p with p = 0., 0.5, and 1.<br><br>RESULTS: The algorithms were tested offline in a pseudo-online manner for features grouped by spatial dimension. A comparison study was conducted using an epidural ECoG dataset recorded from a tetraplegic individual during long-term BCI experiments for controlling a virtual avatar (left/right-hand 3D translation). Novel algorithms showed comparable or better decoding performance than conventional REW-NPLS, which was achieved with sparse models. The proposed algorithms are compatible with real-time CLDA.<br><br>DISCUSSION: The proposed algorithm demonstrated good performance while drastically reducing the computational load and the memory consumption. However, the current study is limited to offline computation on data recorded with a single patient, with penalization restricted to the spatial domain only.}, }
@article {pmid36948359, year = {2023}, author = {Gao, X and Zhang, S and Liu, K and Tan, Z and Zhao, G and Han, Y and Cheng, Y and Li, C and Li, F and Tian, Y and Li, P}, title = {An Adaptive Joint CCA-ICA Method for Ocular Artifact Removal and its Application to Emotion Classification.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109841}, doi = {10.1016/j.jneumeth.2023.109841}, pmid = {36948359}, issn = {1872-678X}, abstract = {BACKGROUND: The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research.<br><br>NEW METHOD: We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals.<br><br>RESULTS: We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition.<br><br>Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition.<br><br>CONCLUSIONS: The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.}, }
@article {pmid36945749, year = {2023}, author = {Lebani, BR and Barcelos, ADS and Gouveia, DSES and Girotti, ME and Remaille, EP and Skaff, M and Almeida, FG}, title = {The role of transurethral resection of prostate (TURP) in patients with underactive bladder: 12 months follow-up in different grades of detrusor contractility.}, journal = {The Prostate}, volume = {}, number = {}, pages = {}, doi = {10.1002/pros.24526}, pmid = {36945749}, issn = {1097-0045}, abstract = {INTRODUCTION AND OBJECTIVE: Male detrusor underactivity (DUA) definition remains controversial and no effective treatment is consolidated. Transurethral resection of the prostate (TURP) is one of the cornerstones surgical treatments recommended in bladder outlet obstruction (BOO). However, the role of prostatic surgery in male DUA is not clear. The primary endpoint was the clinical and voiding improvement based on IPSS and the maximum flow rate in uroflowmetry (Qmax) within 12 months.<br><br>MATERIALS AND METHODS: We analyzed an ongoing prospective database that embraces benign prostata hyperplasia (BPH) male patients with lower urinary tract symptoms who have undergone to TURP. All patients were evaluated pre and postoperatively based on IPSS questionnaires, prostate volume measured by ultrasound, postvoid residual urine volume (PVR), Prostate Specific Antigen measurement and urodynamic study (UDS) before the procedure. After surgery, all patients were evaluated at 1-, 3-, 6- and 12-months. Patients were categorized in 3 groups: Group 1-Detrusor Underactive (Bladder Contractility Index (BCI) [BCI]&#x2009;<&#x2009;100 and BOO index [BOOI]&#x2009;<&#x2009;40); Group 2-Detrusor Underactive and BOO (BCI&#x2009;<&#x2009;100 and BOOI&#x2009;&#x2265;&#x2009;40); Group 3-BOO (BCI&#x2009;&#x2265;&#x2009;100 and BOOI&#x2009;&#x2265;&#x2009;0).<br><br>RESULTS: It was included 158 patients underwent monopolar or bipolar TURP since November 2015 to March 2021. According to UDS, patients were categorized in: group 1 (n&#x2009;=&#x2009;39 patients); group 2 (n&#x2009;=&#x2009;41 patients); group 3 (n&#x2009;=&#x2009;77 patients). Preoperative IPSS was similar between groups (group 1-24.9&#x2009;&#xb1;&#x2009;6.33; group 2-24.8&#x2009;&#xb1;&#x2009;7.33; group 3-24.5&#x2009;&#xb1;&#x2009;6.23). Qmax was statistically lower in the group 2 (group 1-5.43&#x2009;&#xb1;&#x2009;3.69; group 2-3.91&#x2009;&#xb1;&#x2009;2.08; group 3-6.3&#x2009;&#xb1;&#x2009;3.18) as well as greater PVR. The 3 groups presented similar outcomes regard to IPSS score during the follow-up. There was a significant increase in Qmax in the 3 groups. However, group 1 presented the lowest Qmax improvement.<br><br>CONCLUSION: There were different objective outcomes depending on the degree of DUA at 12 months follow-up. Patients with DUA had similar IPSS improvement. However, DUA patients had worst Qmax improvement than men with normal bladder contraction.}, }
@article {pmid36939855, year = {2023}, author = {Karoly, HC and Drennan, ML and Prince, MA and Zulic, L and Dooley, G}, title = {Consuming oral cannabidiol prior to a standard alcohol dose has minimal effect on breath alcohol level and subjective effects of alcohol.}, journal = {Psychopharmacology}, volume = {}, number = {}, pages = {}, pmid = {36939855}, issn = {1432-2072}, support = {UL1 TR002535/TR/NCATS NIH HHS/United States ; K23AA028238/AA/NIAAA NIH HHS/United States ; }, abstract = {RATIONALE: Cannabidiol (CBD) is found in the cannabis plant and has garnered attention as a potential treatment for alcohol use disorder (AUD). CBD reduces alcohol consumption and other markers of alcohol dependence in rodents, but human research on CBD and alcohol is limited. It is unknown whether CBD reduces drinking in humans, and mechanisms through which CBD could impact behavioral AUD phenotypes are unknown.<br><br>OBJECTIVES: This study explores effects of oral CBD on breath alcohol level (BrAC), and subjective effects of alcohol in human participants who report heavy drinking.<br><br>METHODS: In this placebo-controlled, crossover study, participants consumed 30&#xa0;mg CBD, 200&#xa0;mg CBD, or placebo CBD before receiving a standardized alcohol dose. Participants were blind to which CBD dose they received at each session and completed sessions in random order. Thirty-six individuals completed at least one session and were included in analyses.<br><br>RESULTS: Differences in outcomes across the three conditions and by sex were explored using multilevel structural equation models. BrAC fell fastest in the placebo condition, followed by 30&#xa0;mg and 200&#xa0;mg CBD. Stimulation decreased more slowly in the 200&#xa0;mg CBD condition than in placebo (b&#x2009;=&#x2009;&#x2009;-&#x2009;2.38, BCI [-&#x2009;4.46,&#x2009;-&#x2009;.03]). Sedation decreased more slowly in the 30&#xa0;mg CBD condition than in placebo (b&#x2009;=&#x2009;&#x2009;-&#x2009;2.41, BCI [-&#x2009;4.61,&#x2009;-&#x2009;.09]). However, the magnitude of condition differences in BrAC and subjective effects was trivial.<br><br>CONCLUSIONS: CBD has minimal influence on BrAC and subjective effects of alcohol. Further research is needed to test whether CBD impacts alcohol consumption in humans, and if so, what mechanism(s) may explain this effect.}, }
@article {pmid36938361, year = {2023}, author = {Hanna, J and Flöel, A}, title = {An accessible and versatile deep learning-based sleep stage classifier.}, journal = {Frontiers in neuroinformatics}, volume = {17}, number = {}, pages = {1086634}, pmid = {36938361}, issn = {1662-5196}, abstract = {Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.}, }
@article {pmid36937688, year = {2023}, author = {Sawai, S and Murata, S and Fujikawa, S and Yamamoto, R and Shima, K and Nakano, H}, title = {Effects of neurofeedback training combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1148336}, pmid = {36937688}, issn = {1662-4548}, abstract = {INTRODUCTION: Neurofeedback (NFB) training and transcranial direct current stimulation (tDCS) have been shown to individually improve motor imagery (MI) abilities. However, the effect of combining both of them with MI has not been verified. Therefore, the aim of this study was to examine the effect of applying tDCS directly before MI with NFB.<br><br>METHODS: Participants were divided into an NFB group (n = 10) that performed MI with NFB and an NFB + tDCS group (n = 10) that received tDCS for 10 min before MI with NFB. Both groups performed 60 MI trials with NFB. The MI task was performed 20 times without NFB before and after training, and &#x3bc;-event-related desynchronization (ERD) and vividness MI were evaluated.<br><br>RESULTS: &#x3bc;-ERD increased significantly in the NFB + tDCS group compared to the NFB group. MI vividness significantly increased before and after training.<br><br>DISCUSSION: Transcranial direct current stimulation and NFB modulate different processes with respect to MI ability improvement; hence, their combination might further improve MI performance. The results of this study indicate that the combination of NFB and tDCS for MI is more effective in improving MI abilities than applying them individually.}, }
@article {pmid36937679, year = {2023}, author = {Lai, D and Wan, Z and Lin, J and Pan, L and Ren, F and Zhu, J and Zhang, J and Wang, Y and Hao, Y and Xu, K}, title = {Neuronal representation of bimanual arm motor imagery in the motor cortex of a tetraplegia human, a pilot study.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1133928}, pmid = {36937679}, issn = {1662-4548}, abstract = {INTRODUCTION: How the human brain coordinates bimanual movements is not well-established.<br><br>METHODS: Here, we recorded neural signals from a paralyzed individual's left motor cortex during both unimanual and bimanual motor imagery tasks and quantified the representational interaction between arms by analyzing the tuning parameters of each neuron.<br><br>RESULTS: We found a similar proportion of neurons preferring each arm during unimanual movements, however, when switching to bimanual movements, the proportion of contralateral preference increased to 71.8%, indicating contralateral lateralization. We also observed a decorrelation process for each arm's representation across the unimanual and bimanual tasks. We further confined that these changes in bilateral relationships are mainly caused by the alteration of tuning parameters, such as the increased bilateral preferred direction (PD) shifts and the significant suppression in bilateral modulation depths (MDs), especially the ipsilateral side.<br><br>DISCUSSION: These results contribute to the knowledge of bimanual coordination and thus the design of cutting-edge bimanual brain-computer interfaces.}, }
@article {pmid36936191, year = {2023}, author = {Islam, MK and Rastegarnia, A}, title = {Editorial: Recent advances in EEG (non-invasive) based BCI applications.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1151852}, pmid = {36936191}, issn = {1662-5188}, }
@article {pmid36935358, year = {2023}, author = {Gerasimov, JY and Tu, D and Hitaishi, V and Harikesh, PC and Yang, CY and Abrahamsson, T and Rad, M and Donahue, MJ and Ejneby, MS and Berggren, M and Forchheimer, R and Fabiano, S}, title = {A Biologically Interfaced Evolvable Organic Pattern Classifier.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2207023}, doi = {10.1002/advs.202207023}, pmid = {36935358}, issn = {2198-3844}, support = {ERC-2018-ADG/ERC_/European Research Council/International ; }, abstract = {Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process these data into meaningful information, and translate that information into a format that can be interpreted by living systems. Here, the first example of interfacing a hardware-based pattern classifier with a biological nerve is reported. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention by two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of adaptive neural interfaces for closed-loop therapeutic systems.}, }
@article {pmid36933706, year = {2023}, author = {Wang, A and Fan, Z and Zhang, Y and Wang, J and Zhang, X and Wang, P and Mu, W and Zhan, G and Wang, M and Zhang, L and Gan, Z and Kang, X}, title = {Resting-state SEEG-Based Brain Network Analysis for the Detection of Epileptic Area.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109839}, doi = {10.1016/j.jneumeth.2023.109839}, pmid = {36933706}, issn = {1872-678X}, abstract = {BACKGROUND: Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions.<br><br>NEW METHOD: The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes.<br><br>RESULTS: By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p<0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 &#xb1; 0.08 for the epilepsy zone.<br><br>CONCLUSIONS AND SIGNIFICANCE: The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.}, }
@article {pmid36931795, year = {2023}, author = {Johnson, KA and Worbe, Y and Foote, KD and Butson, CR and Gunduz, A and Okun, MS}, title = {Neurosurgical lesioning for Tourette syndrome - Authors' reply.}, journal = {The Lancet. Neurology}, volume = {22}, number = {4}, pages = {292-293}, doi = {10.1016/S1474-4422(23)00079-0}, pmid = {36931795}, issn = {1474-4465}, }
@article {pmid36930206, year = {2023}, author = {Wang, F and Chen, Y and Lin, Y and Wang, X and Li, K and Han, Y and Wu, J and Shi, X and Zhu, Z and Long, C and Hu, X and Duan, S and Gao, Z}, title = {A parabrachial to hypothalamic pathway mediates defensive behavior.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {36930206}, issn = {2050-084X}, abstract = {Defensive behaviors are critical for animal's survival. Both the paraventricular nucleus of the hypothalamus (PVN) and the parabrachial nucleus (PBN) have been shown to be involved in defensive behaviors. However, whether there are direct connections between them to mediate defensive behaviors remains unclear. Here, by retrograde and anterograde tracing, we uncover that cholecystokinin (CCK)-expressing neurons in the lateral PBN (LPB[CCK]) directly project to the PVN. By in vivo fiber photometry recording, we find that LPB[CCK] neurons actively respond to various threat stimuli. Selective photoactivation of LPB[CCK] neurons promotes aversion and defensive behaviors. Conversely, photoinhibition of LPB[CCK] neurons attenuates rat or looming stimuli-induced flight responses. Optogenetic activation of LPB[CCK] axon terminals within the PVN or PVN glutamatergic neurons promotes defensive behaviors. Whereas chemogenetic and pharmacological inhibition of local PVN neurons prevent LPB[CCK]-PVN pathway activation-driven flight responses. These data suggest that LPB[CCK] neurons recruit downstream PVN neurons to actively engage in flight responses. Our study identifies a previously unrecognized role for the LPB[CCK]-PVN pathway in controlling defensive behaviors.}, }
@article {pmid36928694, year = {2023}, author = {Berger, CC and Coppi, S and Ehrsson, HH}, title = {Synchronous motor imagery and visual feedback of finger movement elicit the moving rubber hand illusion, at least in illusion-susceptible individuals.}, journal = {Experimental brain research}, volume = {}, number = {}, pages = {}, pmid = {36928694}, issn = {1432-1106}, abstract = {Recent evidence suggests that imagined auditory and visual sensory stimuli can be integrated with real sensory information from a different sensory modality to change the perception of external events via cross-modal multisensory integration mechanisms. Here, we explored whether imagined voluntary movements can integrate visual and proprioceptive cues to change how we perceive our own limbs in space. Participants viewed a robotic hand wearing a glove repetitively moving its right index finger up and down at a frequency of 1 Hz, while they imagined executing the corresponding movements synchronously or asynchronously (kinesthetic-motor imagery); electromyography (EMG) from the participants' right index flexor muscle confirmed that the participants kept their hand relaxed while imagining the movements. The questionnaire results revealed that the synchronously imagined movements elicited illusory ownership and a sense of agency over the moving robotic hand-the moving rubber hand illusion-compared with asynchronously imagined movements; individuals who affirmed experiencing the illusion with real synchronous movement also did so with synchronous imagined movements. The results from a proprioceptive drift task further demonstrated a shift in the perceived location of the participants' real hand toward the robotic hand in the synchronous versus the asynchronous motor imagery condition. These results suggest that kinesthetic motor imagery can be used to replace veridical congruent somatosensory feedback from a moving finger in the moving rubber hand illusion to trigger illusory body ownership and agency, but only if the temporal congruence rule of the illusion is obeyed. This observation extends previous studies on the integration of mental imagery and sensory perception to the case of multisensory bodily awareness, which has potentially important implications for research into embodiment of brain-computer interface controlled robotic prostheses and computer-generated limbs in virtual reality.}, }
@article {pmid36927003, year = {2023}, author = {Hou, Y and Ling, Y and Wang, Y and Wang, M and Chen, Y and Li, X and Hou, X}, title = {Learning from the Brain: Bioinspired Nanofluidics.}, journal = {The journal of physical chemistry letters}, volume = {}, number = {}, pages = {2891-2900}, doi = {10.1021/acs.jpclett.2c03930}, pmid = {36927003}, issn = {1948-7185}, abstract = {The human brain completes intelligent behaviors such as the generation, transmission, and storage of neural signals by regulating the ionic conductivity of ion channels in neuron cells, which provides new inspiration for the development of ion-based brain-like intelligence. Against the backdrop of the gradual maturity of neuroscience, computer science, and micronano materials science, bioinspired nanofluidic iontronics, as an emerging interdisciplinary subject that focuses on the regulation of ionic conductivity of nanofluidic systems to realize brain-like functionalities, has attracted the attention of many researchers. This Perspective provides brief background information and the state-of-the-art progress of nanofluidic intelligent systems. Two main categories are included: nanofluidic transistors and nanofluidic memristors. The prospects of nanofluidic iontronics' interdisciplinary progress in future artificial intelligence fields such as neuromorphic computing or brain-computer interfaces are discussed. This Perspective aims to give readers a clear understanding of the concepts and prospects of this emerging interdisciplinary field.}, }
@article {pmid36925628, year = {2023}, author = {Carino-Escobar, RI and Rodríguez-García, ME and Carrillo-Mora, P and Valdés-Cristerna, R and Cantillo-Negrete, J}, title = {Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1015464}, pmid = {36925628}, issn = {1662-5218}, abstract = {INTRODUCTION: Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.<br><br>METHODS: An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback.<br><br>RESULTS: Significantly higher BCI performance (65.4 &#xb1; 17.9% with the continuous and 62.1 &#xb1; 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback.<br><br>DISCUSSION: It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.}, }
@article {pmid36924669, year = {2023}, author = {Wei, L and Qi, X and Yu, X and Zheng, Y and Luo, X and Wei, Y and Ni, P and Zhao, L and Wang, Q and Ma, X and Deng, W and Guo, W and Hu, X and Li, T}, title = {3,4-Dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs act as potential AMPA receptor potentiators with antidepressant activity.}, journal = {European journal of medicinal chemistry}, volume = {251}, number = {}, pages = {115252}, doi = {10.1016/j.ejmech.2023.115252}, pmid = {36924669}, issn = {1768-3254}, abstract = {Major depressive disorder is a common psychiatric disorder, with ∼30% of patients suffering from treatment-resistant depression. Based on preclinical studies on ketamine, α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) activation may be a promising therapeutic approach. In this study, we synthesized a series of novel 3,4-dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs and analyzed their potential as AMPAR potentiators. Compounds 5aa and 7k exhibited high potentiation with little agonist activity in a high-throughput screen using a calcium influx assay in cultured hippocampal primary neurons. In rats, compound 7k had better pharmacokinetic properties and oral bioavailability (F = 67.19%); it also exhibited an acceptable safety profile in vital internal organs based on hematoxylin and eosin staining. We found that 7k produced a rapid antidepressant-like effect in chronic restraint stress-induced mice 1 h after intraperitoneal administration. Our study presented a series of novel AMPAR potentiators and identified 7k as a promising drug-like candidate against major depressive disorders.}, }
@article {pmid36923936, year = {2023}, author = {Kong, L and Zhang, D and Huang, S and Lai, J and Lu, L and Zhang, J and Hu, S}, title = {Extracellular Vesicles in Mental Disorders: A State-of-art Review.}, journal = {International journal of biological sciences}, volume = {19}, number = {4}, pages = {1094-1109}, pmid = {36923936}, issn = {1449-2288}, abstract = {Extracellular vesicles (EVs) are nanoscale particles with various physiological functions including mediating cellular communication in the central nervous system (CNS), which indicates a linkage between these particles and mental disorders such as schizophrenia, bipolar disorder, major depressive disorder, etc. To date, known characteristics of mental disorders are mainly neuroinflammation and dysfunctions of homeostasis in the CNS, and EVs are proven to be able to regulate these pathological processes. In addition, studies have found that some cargo of EVs, especially miRNAs, were significantly up- or down-regulated in patients with mental disorders. For many years, interest has been generated in exploring new diagnostic and therapeutic methods for mental disorders, but scale assessment and routine drug intervention are still the first-line applications so far. Therefore, underlying the downstream functions of EVs and their cargo may help uncover the pathogenetic mechanisms of mental disorders as well as provide novel biomarkers and therapeutic candidates. This review aims to address the connection between EVs and mental disorders, and discuss the current strategies that focus on EVs-related psychiatric detection and therapy.}, }
@article {pmid36922925, year = {2023}, author = {Khodaei, F and Sadati, SH and Doost, M and Lashgari, R}, title = {LFP polarity changes across cortical and eccentricity in primary visual cortex.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1138602}, pmid = {36922925}, issn = {1662-4548}, abstract = {Local field potentials (LFPs) can evaluate neural population activity in the cortex and their interaction with other cortical areas. Analyzing current source density (CSD) rather than LFPs is very significant due to the reduction of volume conduction effects. Current sinks are construed as net inward transmembrane currents, while current sources are net outward ones. Despite extensive studies of LFPs and CSDs, their morphology in different cortical layers and eccentricities are still largely unknown. Because LFP polarity changes provide a measure of neural activity, they can be useful in implanting brain-computer interface (BCI) chips and effectively communicating the BCI devices to the brain. We hypothesize that sinks and sources analyses could be a way to quantitatively achieve their characteristics in response to changes in stimulus size and layer-dependent differences with increasing eccentricities. In this study, we show that stimulus properties play a crucial role in determining the flow. The present work focusses on the primary visual cortex (V1). In this study, we investigate a map of the LFP-CSD in V1 area by presenting different stimulus properties (e.g., size and type) in the visual field area of Macaque monkeys. Our aim is to use the morphology of sinks and sources to measure the input and output information in different layers as well as different eccentricities. According to the value of CSDs, the results show that the stimuli smaller than RF's size had lower strength than the others and the larger RF's stimulus size showed smaller strength than the optimized stimulus size, which indicated the suppression phenomenon. Additionally, with the increased eccentricity, CSD's strengths were increased across cortical layers.}, }
@article {pmid36921432, year = {2023}, author = {Rokai, J and Ulbert, I and Márton, G}, title = {Edge computing on TPU for brain implant signal analysis.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {162}, number = {}, pages = {212-224}, doi = {10.1016/j.neunet.2023.02.036}, pmid = {36921432}, issn = {1879-2782}, abstract = {The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.}, }
@article {pmid36925573, year = {2021}, author = {Lehnertz, K and Rings, T and Bröhl, T}, title = {Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks.}, journal = {Frontiers in network physiology}, volume = {1}, number = {}, pages = {755016}, pmid = {36925573}, issn = {2674-0109}, abstract = {Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.}, }
@article {pmid36918388, year = {2023}, author = {Heerspink, HJL and Inker, LA and Tighiouart, H and Collier, WH and Haaland, B and Luo, J and Appel, GB and Chan, TM and Estacio, RO and Fervenza, F and Floege, J and Imai, E and Jafar, TH and Lewis, JB and Kam-Tao Li, P and Locatelli, F and Maes, BD and Perna, A and Perrone, RD and Praga, M and Schena, FP and Wanner, C and Xie, D and Greene, T}, title = {Change in Albuminuria and GFR Slope as Joint Surrogate Endpoints for Kidney Failure: Implications for Phase 2 Clinical Trials in CKD.}, journal = {Journal of the American Society of Nephrology : JASN}, volume = {}, number = {}, pages = {}, doi = {10.1681/ASN.0000000000000117}, pmid = {36918388}, issn = {1533-3450}, abstract = {BACKGROUND: Change in log urinary albumin to creatinine ratio (UACR) and GFR slope are individually used as surrogate endpoints in clinical trials of CKD progression. Whether combining these surrogate endpoints might strengthen inferences about clinical benefit is unknown.<br><br>METHODS: Using Bayesian meta-regressions across 41 randomized trials of CKD progression, we characterized the combined relationship between the treatment effects on the clinical endpoint (sustained doubling of serum creatinine, GFR<15 ml/min per 1.73m2, or kidney failure) and treatment effects on UACR change and chronic GFR slope after 3 months. We applied the results to the design of phase 2 trials based on UACR change and chronic GFR slope in combination.<br><br>RESULTS: Treatment effects on the clinical endpoint were strongly associated with the combination of treatment effects on UACR change and chronic slope. The posterior median meta-regression coefficients for treatment effects were -0.41 (95% Baysian confidence interval [BCI], -0.64 to -0.17) per 1 ml/min per 1.73m2 per year for the treatment effect on GFR slope and -0.06 (95% BCI, -0.90 to 0.77) for the treatment effect on UACR change. The predicted probability of clinical benefit when considering both surrogates was determined primarily by estimated treatment effects on UACR when sample size was small (approximately 60 patients per treatment arm) and follow-up brief (approximately 1 year), with the importance of GFR slope increasing for larger sample sizes and longer follow-up.<br><br>CONCLUSIONS: In phase 2 trials of CKD with sample sizes of 100 to 200 patients per arm and follow-up between 1 and 2 years, combining information from treatment effects on UACR change and GFR slope improved prediction of treatment effects on clinical endpoints.}, }
@article {pmid36915907, year = {2023}, author = {Zhu, Y and Wu, L and Ye, S and Fu, Y and Huang, H and Lai, J and Shi, C and Hu, S}, title = {The Chinese Version of Oxford Depression Questionnaire: A Validation Study in Patients with Mood Disorders.}, journal = {Neuropsychiatric disease and treatment}, volume = {19}, number = {}, pages = {547-556}, pmid = {36915907}, issn = {1176-6328}, abstract = {BACKGROUND: Emotional blunting is prevalent in patients with mood disorders and adversely affects the overall treatment outcome. The Oxford Depression Questionnaire is a validated psychometric instrument for assessing emotional blunting. We aimed to evaluate the reliability and validity of the Chinese version of the ODQ (ODQ) in Chinese patients with mood disorders.<br><br>METHODS: 136 mood disorders patients and 95 healthy control participants were recruited at the First Affiliated Hospital of Zhejiang University, School of Medicine. Patients were assessed using the ODQ, Beck Depression Inventory-II (BDI-II), and Montgomery-Asberg Depression Rating Scale (MADRS). Internal consistency reliability and test-retest reliability were analyzed. Confirmatory factor analysis and correlation analysis were used to evaluate construct and convergent validity.<br><br>RESULTS: A total of 136 patients with mood disorders and 95 healthy controls participated in this study. Cronbach &#x3b1; values were 0.928 (ODQ-20) and 0.945 (ODQ-26). Test-retest reliability coefficients were 0.798 (ODQ-20) and 0.836 (ODQ-26) (p<0.05); intraclass correlation coefficient values were 0.777 (ODQ-20) and 0.781 (ODQ-26) (p<0.01). The score of ODQ was positively correlated with BDI-II and MADRS (r=0.326~0.719, 0.235~0.537, p<0.01). The differences in the ODQ scores between the patient and control groups were statistically significant.<br><br>CONCLUSION: The reliability, structural validity, and criterion validity of the ODQ applied to patients with mood disorders meet the psychometric requirements, and the scale can be used to assess emotional blunting in Chinese patients with mood disorders.}, }
@article {pmid36915631, year = {2023}, author = {McDermott, EJ and Metsomaa, J and Belardinelli, P and Grosse-Wentrup, M and Ziemann, U and Zrenner, C}, title = {Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.}, journal = {Virtual reality}, volume = {27}, number = {1}, pages = {347-369}, pmid = {36915631}, issn = {1359-4338}, abstract = {Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time-frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.}, }
@article {pmid36914265, year = {2023}, author = {Dougherty, LL and Dutta, S and Avasthi, P}, title = {The ERK activator, BCI, inhibits ciliogenesis and causes defects in motor behavior, ciliary gating, and cytoskeletal rearrangement.}, journal = {Life science alliance}, volume = {6}, number = {6}, pages = {}, doi = {10.26508/lsa.202301899}, pmid = {36914265}, issn = {2575-1077}, abstract = {MAPK pathways are well-known regulators of the cell cycle, but they have also been found to control ciliary length in a wide variety of organisms and cell types from Caenorhabditis elegans neurons to mammalian photoreceptors through unknown mechanisms. ERK1/2 is a MAP kinase in human cells that is predominantly phosphorylated by MEK1/2 and dephosphorylated by the phosphatase DUSP6. We have found that the ERK1/2 activator/DUSP6 inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), inhibits ciliary maintenance in Chlamydomonas and hTERT-RPE1 cells and assembly in Chlamydomonas These effects involve inhibition of total protein synthesis, microtubule organization, membrane trafficking, and KAP-GFP motor dynamics. Our data provide evidence for various avenues for BCI-induced ciliary shortening and impaired ciliogenesis that gives mechanistic insight into how MAP kinases can regulate ciliary length.}, }
@article {pmid36913120, year = {2023}, author = {Zhang, Y and Zeng, H and Lou, F and Tan, X and Zhang, X and Chen, G}, title = {SLC45A3 Serves as a Potential Therapeutic Biomarker to Attenuate White Matter Injury After Intracerebral Hemorrhage.}, journal = {Translational stroke research}, volume = {}, number = {}, pages = {}, pmid = {36913120}, issn = {1868-601X}, abstract = {Intracerebral hemorrhage (ICH) is a severe cerebrovascular disease, which impairs patients' white matter even after timely clinical interventions. Indicated by studies in the past decade, ICH-induced white matter injury (WMI) is closely related to neurological deficits; however, its underlying mechanism and pertinent treatment are yet insufficient. We gathered two datasets (GSE24265 and GSE125512), and by taking an intersection among interesting genes identified by weighted gene co-expression networks analysis, we determined target genes after differentially expressing genes in two datasets. Additional single-cell RNA-seq analysis (GSE167593) helped locate the gene in cell types. Furthermore, we established ICH mice models induced by autologous blood or collagenase. Basic medical experiments and diffusion tensor imaging were applied to verify the function of target genes in WMI after ICH. Through intersection and enrichment analysis, gene SLC45A3 was identified as the target one, which plays a key role in the regulation of oligodendrocyte differentiation involving in fatty acid metabolic process, etc. after ICH, and single-cell RNA-seq analysis also shows that it mainly locates in oligodendrocytes. Further experiments verified overexpression of SLC45A3 ameliorated brain injury after ICH. Therefore, SLC45A3 might serve as a candidate therapeutic biomarker for ICH-induced WMI, and overexpression of it may be a potential approach for injury attenuation.}, }
@article {pmid36911809, year = {2023}, author = {Tao, QQ and Lin, RR and Wu, ZY}, title = {Early Diagnosis of Alzheimer's Disease: Moving Toward a Blood-Based Biomarkers Era.}, journal = {Clinical interventions in aging}, volume = {18}, number = {}, pages = {353-358}, pmid = {36911809}, issn = {1178-1998}, }
@article {pmid36909601, year = {2023}, author = {Prescott, RA and Pankow, AP and de Vries, M and Crosse, K and Patel, RS and Alu, M and Loomis, C and Torres, V and Koralov, S and Ivanova, E and Dittmann, M and Rosenberg, BR}, title = {A comparative study of in vitro air-liquid interface culture models of the human airway epithelium evaluating cellular heterogeneity and gene expression at single cell resolution.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.27.530299}, pmid = {36909601}, abstract = {The airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial cultures at air-liquid interface (HAE) are a physiologically relevant in vitro model of this heterogeneous tissue, enabling numerous studies of airway disease [1â€"7] . HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining capacity for differentiation to HAE [5] . However, gene expression and innate immune function in HAE derived from BCi-NS1.1 versus primary cells have not been fully characterized. Here, combining single cell RNA-Seq (scRNA-Seq), immunohistochemistry, and functional experimentation, we confirm at high resolution that BCi-NS1.1 and primary HAE cultures are largely similar in morphology, cell type composition, and overall transcriptional patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1 HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus and Staphylococcus aureus . Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.}, }
@article {pmid36908799, year = {2023}, author = {Du, P and Li, P and Cheng, L and Li, X and Su, J}, title = {Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1132290}, pmid = {36908799}, issn = {1662-4548}, abstract = {INTRODUCTION: Currently, it is still a challenge to detect single-trial P300 from electroencephalography (EEG) signals. In this paper, to address the typical problems faced by existing single-trial P300 classification, such as complex, time-consuming and low accuracy processes, a single-trial P300 classification algorithm based on multiplayer data fusion convolutional neural network (CNN) is proposed to construct a centralized collaborative brain-computer interfaces (cBCI) for fast and highly accurate classification of P300 EEG signals.<br><br>METHODS: In this paper, two multi-person data fusion methods (parallel data fusion and serial data fusion) are used in the data pre-processing stage to fuse multi-person EEG information stimulated by the same task instructions, and then the fused data is fed as input to the CNN for classification. In building the CNN network for single-trial P300 classification, the Conv layer was first used to extract the features of single-trial P300, and then the Maxpooling layer was used to connect the Flatten layer for secondary feature extraction and dimensionality reduction, thereby simplifying the computation. Finally batch normalisation is used to train small batches of data in order to better generalize the network and speed up single-trial P300 signal classification.<br><br>RESULTS: In this paper, the above new algorithms were tested on the Kaggle dataset and the Brain-Computer Interface (BCI) Competition III dataset, and by analyzing the P300 waveform features and EEG topography and the four standard evaluation metrics, namely Accuracy, Precision, Recall and F1-score,it was demonstrated that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed other classification algorithms.<br><br>DISCUSSION: The results show that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed the single-person model, and that the single-trial P300 classification algorithm with two multi-person data fusion CNNs involves smaller models, fewer training parameters, higher classification accuracy and improves the overall P300-cBCI classification rate and actual performance more effectively with a small amount of sample information compared to other algorithms.}, }
@article {pmid36908782, year = {2023}, author = {Zhang, R and Chen, Y and Xu, Z and Zhang, L and Hu, Y and Chen, M}, title = {Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1129049}, pmid = {36908782}, issn = {1662-4548}, abstract = {Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.}, }
@article {pmid36908763, year = {2023}, author = {Zuccaroli, I and Lucke-Wold, B and Palla, A and Eremiev, A and Sorrentino, Z and Zakare-Fagbamila, R and McNulty, J and Christie, C and Chandra, V and Mampre, D}, title = {Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections.}, journal = {OBM neurobiology}, volume = {7}, number = {1}, pages = {}, pmid = {36908763}, issn = {2573-4407}, abstract = {Reported neuro-modulation schemes in the literature are typically classified as closed-loop or open-loop. A novel group of recently developed neuro-modulation devices may be better described as a neural bypass, which attempts to transmit neural data from one location of the nervous system to another. The most common form of neural bypasses in the literature utilize EEG recordings of cortical information paired with functional electrical stimulation for effector muscle output, most commonly for assistive applications and rehabilitation in spinal cord injury or stroke. Other neural bypass locations that have also been described, or may soon be in development, include cortical-spinal bypasses, cortical-cortical bypasses, autonomic bypasses, peripheral-central bypasses, and inter-subject bypasses. The most common recording devices include EEG, ECoG, and microelectrode arrays, while stimulation devices include both invasive and noninvasive electrodes. Several devices are in development to improve the temporal and spatial resolution and biocompatibility for neuronal recording and stimulation. A major barrier to entry includes neuroplasticity and current decoding mechanisms that regularly require retraining. Neural bypasses are a unique class of neuro-modulation. Continued advancement of neural recording and stimulating devices with high spatial and temporal resolution, combined with decoding mechanisms uninhibited by neuroplasticity, can expand the therapeutic capability of neural bypassing. Overall, neural bypasses are a promising modality to improve the treatment of common neurologic disorders, including stroke, spinal cord injury, peripheral nerve injury, brain injury and more.}, }
@article {pmid36907708, year = {2023}, author = {Gavaret, M and Iftimovici, A and Pruvost-Robieux, E}, title = {EEG: Current relevance and promising quantitative analyses.}, journal = {Revue neurologique}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neurol.2022.12.008}, pmid = {36907708}, issn = {0035-3787}, abstract = {Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.}, }
@article {pmid36905065, year = {2023}, author = {Tao, T and Gao, Y and Jia, Y and Chen, R and Li, P and Xu, G}, title = {A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, pmid = {36905065}, issn = {1424-8220}, abstract = {An error-related potential (ErrP) occurs when people's expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain-computer interfaces.}, }
@article {pmid36908334, year = {2022}, author = {Huggins, JE and Krusienski, D and Vansteensel, MJ and Valeriani, D and Thelen, A and Stavisky, S and Norton, JJS and Nijholt, A and Müller-Putz, G and Kosmyna, N and Korczowski, L and Kapeller, C and Herff, C and Halder, S and Guger, C and Grosse-Wentrup, M and Gaunt, R and Dusang, AN and Clisson, P and Chavarriaga, R and Anderson, CW and Allison, BZ and Aksenova, T and Aarnoutse, E}, title = {Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.}, journal = {Brain computer interfaces (Abingdon, England)}, volume = {9}, number = {2}, pages = {69-101}, pmid = {36908334}, issn = {2326-263X}, abstract = {The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.}, }
@article {pmid36905004, year = {2023}, author = {Saibene, A and Caglioni, M and Corchs, S and Gasparini, F}, title = {EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052798}, pmid = {36905004}, issn = {1424-8220}, abstract = {In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.}, }
@article {pmid36904950, year = {2023}, author = {Collazos-Huertas, DF and Álvarez-Meza, AM and Cárdenas-Peña, DA and Castaño-Duque, GA and Castellanos-Domínguez, CG}, title = {Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052750}, pmid = {36904950}, issn = {1424-8220}, abstract = {Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of "poor skill" subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.}, }
@article {pmid36904683, year = {2023}, author = {Oikonomou, VP and Georgiadis, K and Kalaganis, F and Nikolopoulos, S and Kompatsiaris, I}, title = {A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052480}, pmid = {36904683}, issn = {1424-8220}, abstract = {In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).}, }
@article {pmid36904629, year = {2023}, author = {Oikonomou, VP}, title = {Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052425}, pmid = {36904629}, issn = {1424-8220}, abstract = {Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.}, }
@article {pmid36899699, year = {2023}, author = {Niu, K and An, Z and Yao, Z and Chen, C and Yang, L and Xiong, J}, title = {Effects of Different Bedding Materials on Production Performance, Lying Behavior and Welfare of Dairy Buffaloes.}, journal = {Animals : an open access journal from MDPI}, volume = {13}, number = {5}, pages = {}, doi = {10.3390/ani13050842}, pmid = {36899699}, issn = {2076-2615}, abstract = {Different bedding materials have important effects on the behavioristics, production performance and welfare of buffalo. This study aimed to compare the effects of two bedding materials on lying behavior, production performance and animal welfare of dairy buffaloes. More than 40 multiparous lactating buffaloes were randomly divided into two groups, which were raised on fermented manure bedding (FMB) and chaff bedding (CB). The results showed that the application of FMB improved the lying behavior of buffaloes, the average daily lying time (ADLT) of buffaloes in FMB increased by 58 min compared to those in CB, with a significant difference (p < 0.05); the average daily standing time (ADST) decreased by 30 min, with a significant difference (p < 0.05); and the buffalo comfort index (BCI) increased, but the difference was not significant (p > 0.05). The average daily milk yield of buffaloes in FMB increased by 5.78% compared to buffaloes in CB. The application of FMB improved the hygiene of buffaloes. The locomotion score and hock lesion score were not significantly different between the two groups and all buffaloes did not show moderate and severe lameness. The price of FMB was calculated to be 46% of CB, which greatly reduced the cost of bedding material. In summary, FMB has significantly improved the lying behavior, production performance and welfare of buffaloes and significantly reduce the cost of bedding material.}, }
@article {pmid36899597, year = {2023}, author = {Zhao, SN and Cui, Y and He, Y and He, Z and Diao, Z and Peng, F and Cheng, C}, title = {Teleoperation control of a wheeled mobile robot based on Brain-machine Interface.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {2}, pages = {3638-3660}, doi = {10.3934/mbe.2023170}, pmid = {36899597}, issn = {1551-0018}, abstract = {This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.}, }
@article {pmid36899543, year = {2023}, author = {Yang, L and Shi, T and Lv, J and Liu, Y and Dai, Y and Zou, L}, title = {A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {2}, pages = {2482-2500}, doi = {10.3934/mbe.2023116}, pmid = {36899543}, issn = {1551-0018}, abstract = {To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.}, }
@article {pmid36899504, year = {2023}, author = {Gan, L and Yin, X and Huang, J and Jia, B}, title = {Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {2}, pages = {1695-1715}, doi = {10.3934/mbe.2023077}, pmid = {36899504}, issn = {1551-0018}, abstract = {Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.}, }
@article {pmid36896512, year = {2023}, author = {Wu, Z and She, Q and Hou, Z and Li, Z and Tian, K and Ma, Y}, title = {Multi-source online transfer algorithm based on source domain selection for EEG classification.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {3}, pages = {4560-4573}, doi = {10.3934/mbe.2023211}, pmid = {36896512}, issn = {1551-0018}, abstract = {The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.}, }
@article {pmid36883755, year = {2023}, author = {Greenwell, D and Vanderkolff, S and Feigh, J}, title = {Understanding De Novo Learning for Brain Machine Interfaces.}, journal = {Journal of neurophysiology}, volume = {}, number = {}, pages = {}, doi = {10.1152/jn.00496.2022}, pmid = {36883755}, issn = {1522-1598}, abstract = {De novo motor learning is a form of motor learning characterized by the development of an entirely new and distinct motor controller to accommodate a novel motor demand. Inversely, adaptation is a form of motor learning characterized by rapid, unconscious modifications in a previously established motor controller to accommodate small deviations in task demands. Since most of motor learning involves the adaption of previously established motor controllers, de novo learning can be challenging to isolate and observe. The recent publication from Haith et al. (2022) details a novel method to investigate de novo learning using a complex bimanual cursor control task. This research is especially important in the context of future brain machine interface devices that will present users with an entirely novel motor learning demand, requiring de novo learning.}, }
@article {pmid36878831, year = {2023}, author = {Mathon, B and Navarro, V and Lecas, S and Roussel, D and Charpier, S and Carpentier, A}, title = {Safety Profile of Low-Intensity Pulsed Ultrasound-Induced Blood-Brain Barrier Opening in Non-epileptic Mice and in a Mouse Model of Mesial Temporal Lobe Epilepsy.}, journal = {Ultrasound in medicine &amp; biology}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.ultrasmedbio.2023.02.002}, pmid = {36878831}, issn = {1879-291X}, abstract = {OBJECTIVE: It is unknown whether ultrasound-induced blood-brain barrier (BBB) disruption can promote epileptogenesis and how BBB integrity changes over time after sonication.<br><br>METHODS: To gain more insight into the safety profile of ultrasound (US)-induced BBB opening, we determined BBB permeability as well as histological modifications in C57BL/6 adult control mice and in the kainate (KA) model for mesial temporal lobe epilepsy in mice after sonication with low-intensity pulsed ultrasound (LIPU). Microglial and astroglial changes in ipsilateral hippocampus were examined at different time points following BBB disruption by respectively analyzing Iba1 and glial fibrillary acidic protein immunoreactivity. Using intracerebral EEG recordings, we further studied the possible electrophysiological repercussions of a repeated disrupted BBB for seizure generation in nine non-epileptic mice.<br><br>RESULTS: LIPU-induced BBB opening led to transient albumin extravasation and reversible mild astrogliosis, but not to microglial activation in the hippocampus of non-epileptic mice. In KA mice, the transient albumin extravasation into the hippocampus mediated by LIPU-induced BBB opening did not aggravate inflammatory processes and histologic changes that characterize the hippocampal sclerosis. Three LIPU-induced BBB opening did not induce epileptogenicity in non-epileptic mice implanted with depth EEG electrodes.<br><br>CONCLUSION: Our experiments in mice provide persuasive evidence of the safety of LIPU-induced BBB opening as a therapeutic modality for neurological diseases.}, }
@article {pmid36878727, year = {2023}, author = {Ma, J and Hu, Z and Yue, H and Luo, Y and Wang, C and Wu, X and Gu, Y and Wang, L}, title = {GRM2 regulates functional integration of adult-born DGCs by paradoxically modulating MEK/ERK1/2 pathway.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.1886-22.2023}, pmid = {36878727}, issn = {1529-2401}, abstract = {Metabotropic glutamate receptor 2 (GRM2) is highly expressed in hippocampal dentate granule cells (DGCs), regulating synaptic transmission and hippocampal functions. Newborn DGCs are continuously generated throughout life, and express GRM2 when they are mature. However, it remained unclear whether and how GRM2 regulates the development and integration of these newborn neurons. We discovered that the expression of GRM2 in adult-born DGCs increased with neuronal development in mice of both sexes. Lack of GRM2 caused developmental defects of DGCs and impaired hippocampus-dependent cognitive functions. Intriguingly, our data showed that knockdown of Grm2 resulted in decreased b/c-Raf kinases, and paradoxically led to an excessive activation of MEK/ERK1/2 pathway. Inhibition of MEK ameliorated the developmental defects caused by Grm2 knockdown. Together, our results indicate that GRM2 is necessary for the development and functional integration of newborn DGCs in the adult hippocampus through regulating the phosphorylation and activation state of MEK/ERK1/2 pathway.SIGNIFICANCE STATEMENT:Metabotropic glutamate receptor 2 (GRM2) is highly expressed in mature dentate granule cells (DGCs) in the hippocampus. It remains unclear whether GRM2 is required for the development and integration of adult-born DGCs. We provided a series of in vivo and in vitro evidence to show GRM2 regulates the development of adult-born DGCs and their integration into existing hippocampal circuits. Lack of GRM2 in a cohort of newborn DGCs impaired object-to-location memory in mice. Moreover, we revealed that GRM2 knockdown paradoxically upregulated MEK/ERK1/2 pathway by suppressing b/c-Raf in developing neurons, which is likely a common mechanism underlying the regulation of the development of neurons expressing GRM2. Thus, Raf/MEK/ERK1/2 pathway could be a potential target for brain diseases related to GRM2 abnormality.}, }
@article {pmid36877853, year = {2023}, author = {Qi, HX and Reed, JL and Liao, CC and Kaas, JH}, title = {Regressive changes in sizes of somatosensory cuneate nucleus after sensory loss in primates.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {120}, number = {11}, pages = {e2222076120}, doi = {10.1073/pnas.2222076120}, pmid = {36877853}, issn = {1091-6490}, abstract = {Neurons in the early stages of processing sensory information suffer transneuronal atrophy when deprived of their activating inputs. For over 40 y, members of our laboratory have studied the reorganization of the somatosensory cortex during and after recovering from different types of sensory loss. Here, we took advantage of the preserved histological material from these studies of the cortical effects of sensory loss to evaluate the histological consequences in the cuneate nucleus of the lower brainstem and the adjoining spinal cord. The neurons in the cuneate nucleus are activated by touch on the hand and arm, and relay this activation to the contralateral thalamus, and from the thalamus to the primary somatosensory cortex. Neurons deprived of activating inputs tend to shrink and sometimes die. We considered the effects of differences in species, type and extent of sensory loss, recovery time after injury, and age at the time of injury on the histology of the cuneate nucleus. The results indicate that all injuries that deprived part or all of the cuneate nucleus of sensory activation result in some atrophy of neurons as reflected by a decrease in nucleus size. The extent of the atrophy is greater with greater sensory loss and with longer recovery times. Based on supporting research, atrophy appears to involve a reduction in neuron size and neuropil, with little or no neuron loss. Thus, the potential exists for restoring the hand to cortex pathway with brain-machine interfaces, for bionic prosthetics, or biologically with hand replacement surgery.}, }
@article {pmid36877440, year = {2023}, author = {He, C and Duan, S}, title = {Novel Insight into Glial Biology and Diseases.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36877440}, issn = {1995-8218}, }
@article {pmid36876801, year = {2023}, author = {Kumar, A and Sah, DK and Khanna, K and Rai, Y and Yadav, AK and Ansari, MS and Bhatt, AN}, title = {A calcium and zinc composite alginate hydrogel for pre-hospital hemostasis and wound care.}, journal = {Carbohydrate polymers}, volume = {299}, number = {}, pages = {120186}, doi = {10.1016/j.carbpol.2022.120186}, pmid = {36876801}, issn = {1879-1344}, abstract = {We developed, characterized, and examined the hemostatic potential of sodium alginate-based Ca[2+] and Zn[2+] composite hydrogel (SA-CZ). SA-CZ hydrogel showed substantial in-vitro efficacy, as observed by the significant reduction in coagulation time with better blood coagulation index (BCI) and no evident hemolysis in human blood. SA-CZ significantly reduced bleeding time (≈60 %) and mean blood loss (≈65 %) in the tail bleeding and liver incision in the mice hemorrhage model (p ≤ 0.001). SA-CZ also showed enhanced cellular migration (1.58-fold) in-vitro and improved wound closure (≈70 %) as compared with betadine (≈38 %) and saline (≈34 %) at the 7th-day post-wound creation in-vivo (p < 0.005). Subcutaneous implantation and intra-venous gamma-scintigraphy of hydrogel revealed ample body clearance and non-considerable accumulation in any vital organ, proving its non-thromboembolic nature. Overall, SA-CZ showed good biocompatibility along with efficient hemostasis and wound healing qualities, making it suitable as a safe and effective aid for bleeding wounds.}, }
@article {pmid36875646, year = {2023}, author = {Porcaro, C and Avanaki, K and Arias-Carrion, O and Mørup, M}, title = {Editorial: Combined EEG in research and diagnostics: Novel perspectives and improvements.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1152394}, pmid = {36875646}, issn = {1662-4548}, }
@article {pmid36875236, year = {2023}, author = {Zhang, J and Gao, S and Zhou, K and Cheng, Y and Mao, S}, title = {An online hybrid BCI combining SSVEP and EOG-based eye movements.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1103935}, pmid = {36875236}, issn = {1662-5161}, abstract = {Hybrid brain-computer interface (hBCI) refers to a system composed of a single-modality BCI and another system. In this paper, we propose an online hybrid BCI combining steady-state visual evoked potential (SSVEP) and eye movements to improve the performance of BCI systems. Twenty buttons corresponding to 20 characters are evenly distributed in the five regions of the GUI and flash at the same time to arouse SSVEP. At the end of the flash, the buttons in the four regions move in different directions, and the subject continues to stare at the target with eyes to generate the corresponding eye movements. The CCA method and FBCCA method were used to detect SSVEP, and the electrooculography (EOG) waveform was used to detect eye movements. Based on the EOG features, this paper proposes a decision-making method based on SSVEP and EOG, which can further improve the performance of the hybrid BCI system. Ten healthy students took part in our experiment, and the average accuracy and information transfer rate of the system were 94.75% and 108.63 bits/min, respectively.}, }
@article {pmid36868861, year = {2023}, author = {Wang, Y and Lin, J and Li, J and Yan, L and Li, W and He, X and Ma, H}, title = {Chronic neuronal inactivity utilizes the mTOR-TFEB pathway to drive transcription-dependent autophagy for homeostatic up-scaling.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.0146-23.2023}, pmid = {36868861}, issn = {1529-2401}, abstract = {Activity-dependent changes in protein expression are critical for neuronal plasticity, a fundamental process for the processing and storage of information in the brain. Among the various forms of plasticity, homeostatic synaptic up-scaling is unique in that it is induced primarily by neuronal inactivity. However, precisely how the turnover of synaptic proteins occurs in this homeostatic process remains unclear. Here, we report that chronically inhibiting neuronal activity in primary cortical neurons prepared from E18 Sprague-Dawley rats (both sexes) induces autophagy, thereby regulating key synaptic proteins for up-scaling. Mechanistically, chronic neuronal inactivity causes dephosphorylation of ERK and mTOR, which induces TFEB-mediated cytonuclear signaling and drives transcription-dependent autophagy to regulate αCaMKII and PSD95 during synaptic up-scaling. Together, these findings suggest that mTOR-dependent autophagy, which is often triggered by metabolic stressors such as starvation, is recruited and sustained during neuronal inactivity in order to maintain synaptic homeostasis, a process that ensures proper brain function and if impaired can cause neuropsychiatric disorders such as autism.SIGNIFICANCE STATEMENT:In the mammalian brain, protein turnover is tightly controlled by neuronal activation to ensure key neuronal functions during long-lasting synaptic plasticity. However, a long-standing question is how this process occurs during synaptic up-scaling, a process that requires protein turnover but is induced by neuronal inactivation. Here, we report that mTOR-dependent signaling-which is often triggered by metabolic stressors such as starvation-is "hijacked" by chronic neuronal inactivation, which then serves as a nucleation point for TFEB cytonuclear signaling that drives transcription-dependent autophagy for up-scaling. These results provide the first evidence of a physiological role of mTOR-dependent autophagy in enduing neuronal plasticity, thereby connecting major themes in cell biology and neuroscience via a servo loop that mediates autoregulation in the brain.}, }
@article {pmid36868167, year = {2023}, author = {Yang, Y and Zhang, F and Gao, X and Feng, L and Xu, K}, title = {Progressive alterations in electrophysiological and epileptic network properties during the development of temporal lobe epilepsy in rats.}, journal = {Epilepsy &amp; behavior : E&amp;B}, volume = {141}, number = {}, pages = {109120}, doi = {10.1016/j.yebeh.2023.109120}, pmid = {36868167}, issn = {1525-5069}, abstract = {OBJECTIVE: Refractory temporal lobe epilepsy (TLE) with recurring seizures causing continuing pathological changes in neural reorganization. There is an incomplete understanding of how spatiotemporal electrophysiological characteristics changes during the development of TLE. Long-term multi-site epilepsy patients' data is hard to obtain. Thus, our study relied on animal models to reveal the changes in electrophysiological and epileptic network characteristics systematically.<br><br>METHODS: Long-term local field potentials (LFPs) were recorded over a period of 1 to 4&#xa0;months from 6 pilocarpine-treated TLE rats. We compared variations of seizure onset zone (SOZ), seizure onset pattern (SOP), the latency of seizure onsets, and functional connectivity network from 10-channel LFPs between the early and late stages. Moreover, three machine learning classifiers trained by early-stage data were used to test seizure detection performance in the late stage.<br><br>RESULTS: Compared to the early stage, the earliest seizure onset was more frequently detected in hippocampus areas in the late stage. The latency of seizure onsets between electrodes became shorter. Low-voltage fast activity (LVFA) was the most common SOP and the proportion of it increased in the late stage. Different brain states were observed during seizures using Granger causality (GC). Moreover, seizure detection classifiers trained by early-stage data were less accurate when tested in late-stage data.<br><br>SIGNIFICANCE: Neuromodulation especially closed-loop deep brain stimulation (DBS) is effective in the treatment of refractory TLE. Although the frequency or amplitude of the stimulation is generally adjusted in existing closed-loop DBS devices in clinical usage, the adjustment rarely considers the pathological progression of chronic TLE. This suggests that an important factor affecting the therapeutic effect of neuromodulation may have been overlooked. The present study reveals time-varying electrophysiological and epileptic network properties in chronic TLE rats and indicates that classifiers of seizure detection and neuromodulation parameters might be designed to adapt to the current state dynamically with the progression of epilepsy.}, }
@article {pmid36866539, year = {2023}, author = {Chen, S and Guan, X and Xie, L and Liu, C and Li, C and He, M and Hu, J and Fan, H and Li, Q and Xie, L and Yang, M and Zhang, X and Xiao, S and Tang, J}, title = {Aloe-emodin targets multiple signaling pathways by blocking ubiquitin-mediated degradation of DUSP1 in nasopharyngeal carcinoma cells.}, journal = {Phytotherapy research : PTR}, volume = {}, number = {}, pages = {}, doi = {10.1002/ptr.7793}, pmid = {36866539}, issn = {1099-1573}, abstract = {Aloe-emodin (AE) has been shown to inhibit the proliferation of several cancer cell lines, including human nasopharyngeal carcinoma (NPC) cell lines. In this study, we confirmed that AE inhibited malignant biological behaviors, including cell viability, abnormal proliferation, apoptosis, and migration of NPC cells. Western blotting analysis revealed that AE upregulated the expression of DUSP1, an endogenous inhibitor of multiple cancer-associated signaling pathways, resulting in blockage of the extracellular signal-regulated kinase (ERK)-1/2, protein kinase B (AKT), and p38-mitogen activated protein kinase（p38-MAPK） signaling pathways in NPC cell lines. Moreover, the selective inhibitor of DUSP1, BCI-hydrochloride, partially reversed the AE-induced cytotoxicity and blocked the aforementioned signaling pathways in NPC cells. In addition, the binding between AE and DUSP1 was predicted via molecular docking analysis using AutoDock-Vina software and further verified via a microscale thermophoresis assay. The binding amino acid residues were adjacent to the predicted ubiquitination site (Lys192) of DUSP1. Immunoprecipitation with the ubiquitin antibody, ubiquitinated DUSP1 was shown to be upregulated by AE. Our findings revealed that AE can stabilize DUSP1 by blocking its ubiquitin-proteasome-mediated degradation and proposed an underlying mechanism by which AE-upregulated DUSP1 may potentially target multiple pathways in NPC cells.}, }
@article {pmid36866306, year = {2023}, author = {Patel, HH and Berlinberg, EJ and Nwachukwu, B and Williams, RJ and Mandelbaum, B and Sonkin, K and Forsythe, B}, title = {Quadriceps Weakness is Associated with Neuroplastic Changes Within Specific Corticospinal Pathways and Brain Areas After Anterior Cruciate Ligament Reconstruction: Theoretical Utility of Motor Imagery-Based Brain-Computer Interface Technology for Rehabilitation.}, journal = {Arthroscopy, sports medicine, and rehabilitation}, volume = {5}, number = {1}, pages = {e207-e216}, pmid = {36866306}, issn = {2666-061X}, abstract = {UNLABELLED: Persistent quadriceps weakness is a problematic sequela of anterior cruciate ligament reconstruction (ACLR). The purposes of this review are to summarize neuroplastic changes after ACL reconstruction; provide an overview of a promising interventions, motor imagery (MI), and its utility in muscle activation; and propose a framework using a brain-computer interface (BCI) to augment quadriceps activation. A literature review of neuroplastic changes, MI training, and BCI-MI technology in postoperative neuromuscular rehabilitation was conducted in PubMed, Embase, and Scopus. Combinations of the following search terms were used to identify articles: "quadriceps muscle," "neurofeedback," "biofeedback," "muscle activation," "motor learning," "anterior cruciate ligament," and "cortical plasticity." We found that ACLR disrupts sensory input from the quadriceps, which results in reduced sensitivity to electrochemical neuronal signals, an increase in central inhibition of neurons regulating quadriceps control and dampening of reflexive motor activity. MI training consists of visualizing an action, without physically engaging in muscle activity. Imagined motor output during MI training increases the sensitivity and conductivity of corticospinal tracts emerging from the primary motor cortex, which helps "exercise" the connections between the brain and target muscle tissues. Motor rehabilitation studies using BCI-MI technology have demonstrated increased excitability of the motor cortex, corticospinal tract, spinal motor neurons, and disinhibition of inhibitory interneurons. This technology has been validated and successfully applied in the recovery of atrophied neuromuscular pathways in stroke patients but has yet to be investigated in peripheral neuromuscular insults, such as ACL injury and reconstruction. Well-designed clinical studies may assess the impact of BCI on clinical outcomes and recovery time. Quadriceps weakness is associated with neuroplastic changes within specific corticospinal pathways and brain areas. BCI-MI shows strong potential for facilitating recovery of atrophied neuromuscular pathways after ACLR and may offer an innovative, multidisciplinary approach to orthopaedic care.<br><br>LEVEL OF EVIDENCE: V, expert opinion.}, }
@article {pmid36865207, year = {2023}, author = {Forenzo, D and Liu, Y and Kim, J and Ding, Y and Yoon, T and He, B}, title = {Integrating simultaneous motor imagery and spatial attention for EEG-BCI control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.20.529307}, pmid = {36865207}, abstract = {OBJECTIVE: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control.<br><br>METHODS: We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI).<br><br>RESULTS: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), statistically outperforms MI alone (42%), and was higher, but not statistically significant, than OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA.<br><br>CONCLUSION: Integrating MI and OSA leads to improved performance over MI alone at the group level and is the best BCI paradigm option for some subjects.<br><br>SIGNIFICANCE: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.}, }
@article {pmid36863014, year = {2023}, author = {Li, G and Liu, Y and Chen, Y and Li, M and Song, J and Li, K and Zhang, Y and Hu, L and Qi, X and Wan, X and Liu, J and He, Q and Zhou, H}, title = {Polyvinyl alcohol/polyacrylamide double-network hydrogel-based semi-dry electrodes for robust electroencephalography recording at hairy scalp for noninvasive brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acc098}, pmid = {36863014}, issn = {1741-2552}, abstract = {OBJECTIVE: Reliable and user-friendly electrodes can continuously and real-time capture the electroencephalography signals, which is essential for real-life brain-computer interfaces (BCIs). This study develops a flexible, durable, and low-contact-impedance polyvinyl alcohol/polyacrylamide double-network hydrogel (PVA/PAM DNH)-based semi-dry electrode for robust electroencephalography recording at hairy scalp.<br><br>APPROACH: The PVA/PAM DNHs are developed using a cyclic freeze-thaw strategy and used as a saline reservoir for semi-dry electrodes. The PVA/PAM DNHs steadily deliver trace amounts of saline onto the scalp, enabling low and stable electrode-scalp impedance. The hydrogel also conforms well to the wet scalp, stabilizing the electrode-scalp interface. The feasibility of the real-life BCIs is validated by conducting four classic BCI paradigms on 16 participants.<br><br>MAIN RESULTS: The results show that the PVA/PAM DNHs with 7.5%wt% PVA achieve a satisfactory trade-off between the saline load-unloading capacity and the compressive strength. The proposed semi-dry electrode exhibits a low contact impedance (18 &#xb1; 8.9 k&#x3a9; at 10 Hz), a small offset potential (0.46 mV), and negligible potential drift (1.5 &#xb1; 0.4 &#x3bc;V/min). The temporal cross-correlation between the semi-dry and wet electrodes is 0.91, and the spectral coherence is higher than 0.90 at frequencies below 45 Hz. Furthermore, no significant differences are present in BCI classification accuracy between these two typical electrodes.<br><br>SIGNIFICANCE: Based on the durability, rapid setup, wear-comfort, and robust signals of the developed hydrogel, PVA/PAM DNH-based semi-dry electrodes are a promising alternative to wet electrodes in real-life BCIs. &#xD.}, }
@article {pmid36780560, year = {2023}, author = {Chen, X and Ma, R and Zhang, W and Zeng, GQ and Wu, Q and Yimiti, A and Xia, X and Cui, J and Liu, Q and Meng, X and Bu, J and Chen, Q and Pan, Y and Yu, NX and Wang, S and Deng, ZD and Sack, AT and Laughlin, MM and Zhang, X}, title = {Alpha oscillatory activity is causally linked to working memory retention.}, journal = {PLoS biology}, volume = {21}, number = {2}, pages = {e3001999}, doi = {10.1371/journal.pbio.3001999}, pmid = {36780560}, issn = {1545-7885}, abstract = {Although previous studies have reported correlations between alpha oscillations and the "retention" subprocess of working memory (WM), causal evidence has been limited in human neuroscience due to the lack of delicate modulation of human brain oscillations. Conventional transcranial alternating current stimulation (tACS) is not suitable for demonstrating the causal evidence for parietal alpha oscillations in WM retention because of its inability to modulate brain oscillations within a short period (i.e., the retention subprocess). Here, we developed an online phase-corrected tACS system capable of precisely correcting for the phase differences between tACS and concurrent endogenous oscillations. This system permits the modulation of brain oscillations at the target stimulation frequency within a short stimulation period and is here applied to empirically demonstrate that parietal alpha oscillations causally relate to WM retention. Our experimental design included both in-phase and anti-phase alpha-tACS applied to participants during the retention subprocess of a modified Sternberg paradigm. Compared to in-phase alpha-tACS, anti-phase alpha-tACS decreased both WM performance and alpha activity. These findings strongly support a causal link between alpha oscillations and WM retention and illustrate the broad application prospects of phase-corrected tACS.}, }
@article {pmid36862765, year = {2023}, author = {Mao, C and Xiao, P and Tao, XN and Qin, J and He, QT and Zhang, C and Guo, SC and Du, YQ and Chen, LN and Shen, DD and Yang, ZS and Zhang, HQ and Huang, SM and He, YH and Cheng, J and Zhong, YN and Shang, P and Chen, J and Zhang, DL and Wang, QL and Liu, MX and Li, GY and Guo, Y and Xu, HE and Wang, C and Zhang, C and Feng, S and Yu, X and Zhang, Y and Sun, JP}, title = {Unsaturated bond recognition leads to biased signal in a fatty acid receptor.}, journal = {Science (New York, N.Y.)}, volume = {}, number = {}, pages = {eadd6220}, doi = {10.1126/science.add6220}, pmid = {36862765}, issn = {1095-9203}, abstract = {Individual free fatty acids (FAs) play important roles in metabolic homeostasis, many via engagement with more than 40 GPCRs. Searching for receptors to sense beneficial ω-3 FAs of fish oil enabled the identification of GPR120, involving with a spectrum of metabolic diseases. Here, we report six cryo-EM structures of GPR120 in complex with FA hormones or TUG891 and Gi or Giq trimers. Aromatic residues inside the GPR120 ligand pocket were responsible for recognizing different double-bond positions of these FAs and connect ligand recognition to distinct effector coupling. We also investigated synthetic ligand selectivity and the structural basis of missense single nucleotide polymorphisms. We reveal how GPR120 differentiates rigid double bonds and flexible single bonds and may facilitate rational drug design targeting to GPR120.}, }
@article {pmid36861900, year = {2023}, author = {Shah, AM}, title = {New development in brain-computer interface platforms: 1-year results from the SWITCH trial.}, journal = {Artificial organs}, volume = {}, number = {}, pages = {}, doi = {10.1111/aor.14511}, pmid = {36861900}, issn = {1525-1594}, abstract = {Synchron publishes SWITCH trial results demonstrating the safety and efficacy of stentrode™ device. The stentrode™ is an endovascularly implanted brain-computer interface communication device capable of relaying neural activity from the motor cortex of paralyzed patients. The platform has been used to recover speech.}, }
@article {pmid36861042, year = {2023}, author = {Valeriani, D and Cecotti, H and Thelen, A and Herff, C}, title = {Editorial: Translational brain-computer interfaces: From research labs to the market and back.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1152466}, pmid = {36861042}, issn = {1662-5161}, }
@article {pmid36860620, year = {2023}, author = {Huang, G and Zhao, Z and Zhang, S and Hu, Z and Fan, J and Fu, M and Chen, J and Xiao, Y and Wang, J and Dan, G}, title = {Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1122661}, pmid = {36860620}, issn = {1662-4548}, abstract = {INTRODUCTION: Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal.<br><br>METHODS: To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives.<br><br>RESULTS: Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks.<br><br>DISCUSSION: All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.}, }
@article {pmid36860616, year = {2023}, author = {Ivanov, N and Chau, T}, title = {Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1108889}, pmid = {36860616}, issn = {1662-5188}, abstract = {Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.}, }
@article {pmid36856917, year = {2023}, author = {Song, Y and Sun, Z and Sun, W and Luo, M and Du, Y and Jing, J and Wang, Y}, title = {Neuroplasticity Following Stroke from a Functional Laterality Perspective: A fNIRS Study.}, journal = {Brain topography}, volume = {}, number = {}, pages = {}, pmid = {36856917}, issn = {1573-6792}, abstract = {To explore alterations of resting-state functional connectivity (rsFC) in sensorimotor cortex following strokes with left or right hemiplegia considering the lateralization and neuroplasticity. Seventy-three resting-state functional near-infrared spectroscopy (fNIRS) files were selected, including 26 from left hemiplegia (LH), 21 from right hemiplegia (RH) and 26 from normal controls (NC) group. Whole-brain analyses matching the Pearson correlation were used for rsFC calculations. For right-handed normal controls, rsFC of motor components (M1 and M2) in the left hemisphere displayed a prominent intensity in comparison with the right hemisphere (p < 0.05), while for stroke groups, this asymmetry has disappeared. Additionally, RH rather than LH showed stronger rsFC between left S1 and left M1 in contrast to normal controls (p < 0.05), which correlated inversely with motor function (r = - 0.53, p < 0.05). Regarding M1, rsFC within ipsi-lesioned M1 has a negative correlation with motor function of the affected limb (r = - 0.60 for the RH group and - 0.43 for the LH group, p < 0.05). The rsFC within contra-lesioned M1 that innervates the normal side was weakened compared with that of normal controls (p < 0.05). Stronger rsFC of motor components in left hemisphere was confirmed by rs-fNIRS as the "secret of dominance" for the first time, while post-stroke hemiplegia broke this cortical asymmetry. Meanwhile, a statistically strengthened rsFC between left S1 and M1 only in right-hemiplegia group may act as a compensation for the impairment of the dominant side. This research has implications for brain-computer interfaces synchronizing sensory feedback with motor performance and transcranial magnetic regulation for cortical excitability to induce cortical plasticity.}, }
@article {pmid36855290, year = {2023}, author = {Çevik Saldıran, T and Kara, &#x130; and Dinçer, E and Öztürk, &#xd6; and Çakıcı, R and Burroughs, T}, title = {Cross-cultural adaptation and validation of Diabetes Quality of Life Brief Clinical Inventory in Turkish patients with type 2 diabetes mellitus.}, journal = {Disability and rehabilitation}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/09638288.2023.2182917}, pmid = {36855290}, issn = {1464-5165}, abstract = {PURPOSE: To translate and culturally adapt the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) into Turkish and assess the psychometric properties of the translated version.<br><br>METHODS: A forward-backward translation process was conducted in conformity with international guidelines. A total of 150 patients with type 2 diabetes mellitus (T2DM) completed the Turkish version of DQoL-BCI (DQoL-BCI-Tr). The factor structure, test-retest reliability, and construct validity were evaluated.<br><br>RESULTS: In the DQoL-BCI-Tr, the three-factor structure was found optimal and explained 68.7% of the variance. The DQoL-BCI-Tr showed excellent internal consistency (Cronbach's alpha = 0.90) and test-retest reliability (ICC = 0.98). Cronbach's alpha values ranged from 0.85 to 0.91 for subscales (satisfaction, worry, impact). A negative correlation was found between the total scores of the DQoL-BCI-Tr and the EuroQoL-5 dimensions (EQ-5D) indexes (r=&#x2009;-0.22, p&#x2009;<&#x2009;0.01). The DQoL-BCI-Tr total score and satisfaction and worry subscale scores differentiated between groups defined by glycated hemoglobin (HbA1c>9%) and the use of insulin.<br><br>CONCLUSIONS: The study results showed that the DQoL-BCI-Tr can be served as a reliable and valid instrument to obtain information from Turkish patients with T2DM diagnosis, including satisfaction with treatment, the impact of the disease, and worry about the social/vocational issues.Implications for rehabilitationThe Turkish version of the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) is a valid and reliable instrument.The DQoL-BCI Questionnaire in Turkish (DQoL-BCI-Tr) is an easy and quick way to determine satisfaction with treatment, impact of disease, and worry about the social/vocational issues.The DQoL-BCI-Tr is a reliable instrument for assessing disease-specific effects, emotional loads, and satisfaction of Turkish patients with type 2 diabetes in clinical and research settings.}, }
@article {pmid36854561, year = {2023}, author = {Zheng, C and Liu, Y and Xiao, X and Zhou, X and Xu, F and Xu, M and Ming, D}, title = {[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {40}, number = {1}, pages = {155-162}, doi = {10.7507/1001-5515.202205090}, pmid = {36854561}, issn = {1001-5515}, abstract = {Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.}, }
@article {pmid36854262, year = {2022}, author = {Dadarlat, MC and Canfield, RA and Orsborn, AL}, title = {Neural Plasticity in Sensorimotor Brain-Machine Interfaces.}, journal = {Annual review of biomedical engineering}, volume = {}, number = {}, pages = {}, doi = {10.1146/annurev-bioeng-110220-110833}, pmid = {36854262}, issn = {1545-4274}, abstract = {Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 25 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.}, }
@article {pmid36854181, year = {2023}, author = {Zhang, Y and Qiu, S and He, H}, title = {Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbfdf}, pmid = {36854181}, issn = {1741-2552}, abstract = {A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. MI-BCI systems based on a single modality have been widely researched in recent decades. Lately, along with the development of neuroimaging methods, multimodal MI-BCI studies that use multiple neural signals have been proposed, which are promising for enhancing the decoding accuracy of MI-BCI. Multimodal MI data contain rich common and complementary information. Effective feature representations are helpful to promote the performance of classification tasks. Thus, it is very important to explore and extract features with higher separability and robustness from the rich information in multimodal data. Approach: In this study, a five-class motor imagery experiment was designed. Electroencephalography and functional near infrared spectroscopy data were collected simultaneously. A multimodal MI decoding neural network was proposed. In this network, to enhance the feature representation, the heterogeneous data of different modalities in the spatial dimension were aligned through the proposed spatial alignment losses. Also, the multimodal features were aligned and fused in the temporal dimension by an attention-based modality fusion module. Main results and Significance: The collected dataset was analyzed from temporal, spatial and frequency perspectives. The results showed that the multimodal data contain visually separable motor imagery patterns. The experimental results show that the proposed decoding method achieved the highest decoding accuracy among the compared methods on the self-collected dataset and a public dataset. Ablation results show that each part of the proposed method is effective. Compared with single-modality decoding, the proposed method obtained 4.6% higher decoding accuracy on the self-collected dataset. This indicates that the proposed method can improve the performance of multimodal MI decoding. This study provides a new approach for capturing the rich information in multimodal MI data and enhancing multimodal MI-BCI decoding accuracy. .}, }
@article {pmid36851970, year = {2023}, author = {Amiri, M and Nazari, S and Jafari, AH and Makkiabadi, B}, title = {A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and experimental studies.}, journal = {Heliyon}, volume = {9}, number = {3}, pages = {e13766}, pmid = {36851970}, issn = {2405-8440}, abstract = {BACKGROUND: The bidirectional brain-machine interfaces algorithms are machines that decode neural response in order to control the external device and encode position of artificial limb to proper electrical stimulation, so that the interface between brain and machine closes. Most BMI researchers typically consider four basic elements: recording technology to extract brain activity, decoding algorithm to translate brain activity to the predicted movement of the external device, external device (prosthetic limb such as a robotic arm), and encoding interface to convert the motion of the external machine to set of the electrical stimulation of the brain.<br><br>NEW METHOD: In this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for sensory cortex (S1) connected to the neural network model of motor cortex (M1) considering the topographic mapping between S1 and M1. We use 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes include inhibitory and excitatory neurons and synapses. Next, we develop a new BMI algorithm based on neural activity. The main concept of this BMI algorithm is to close the loop between brain and mechaical external device.<br><br>RESULTS: The sensory interface as encoding algorithm convert the location of the external device (artificial limb) into the electrical stimulation which excite the S1 model. The motor interface as decoding algorithm convert neural recordings from the M1 model into a force which causes the movement of the external device. We present the simulation results for the on line BMI which means that there is a real time information exchange between 9 boxes and 4 boxes of S1-M1 network model and the external device. Also, off line information exchange between brain of five anesthetized rats and externnal device was performed. The proposed BMI algorithm has succeeded in controlling the movement of the mechanical arm towards the target area on simulation and experimental data, so that the BMI algorithm shows acceptable WTPE and the average number of iterations of the algorithm in reaching artificial limb to the target region.Comparison with existing methods and Conclusions: In order to confirm the simulation results the 9-box model of S1-M1 network was developed and the valid "spike train" algorithm, which has good results on real data, is used to compare the performance accuracy of the proposed BMI algorithm versus "spike train" algorithm on simulation and off line experimental data of anesthetized rats. Quantitative and qualitative results confirm the proper performance of the proposed algorithm compared to algorithm "spike train" on simulations and experimental data.}, }
@article {pmid36851960, year = {2023}, author = {Lin, CL and Chen, LT}, title = {Improvement of brain-computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP.}, journal = {Heliyon}, volume = {9}, number = {3}, pages = {e13745}, pmid = {36851960}, issn = {2405-8440}, abstract = {Motor imagery (MI) can produce a specific brain pattern when the subject imagines performing a particular action without any actual body movements. According to related previous research, the improvement of the training of MI brainwaves can be adopted by feedback methods in which the analysis of brainwave characteristics is very important. The aim of this study was to improve the subject's MI and the accuracy of classification. In order to ameliorate the accuracy of the MI of the left and right hand, the present study designed static and dynamic visual stimuli in experiments so as to evaluate which one can improve subjects' imagination training. Additionally, the filter bank common spatial pattern (FBCSP) method was used to divide the frequency band range of the brainwaves into multiple segments, following which linear discriminant analysis (LDA) was adopted for classification. The results revealed that the averaged false positive rate (FPR) under FBCSP-LDA in the dynamic MI experiment was the lowest FPR (23.76%). As such, this study suggested that a combination of the dynamic MI experiment and the FBCSP-LDA method improved the overall prediction error rate and ameliorated the performance of the MI brain-computer interface.}, }
@article {pmid36850850, year = {2023}, author = {Ghodousi, M and Pousson, JE and Bernhofs, V and Griškova-Bulanova, I}, title = {Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {4}, pages = {}, pmid = {36850850}, issn = {1424-8220}, abstract = {A Brain-Computer Music Interface (BCMI) system may be designed to harness electroencephalography (EEG) signals for control over musical outputs in the context of emotionally expressive performance. To develop a real-time BCMI system, accurate and computationally efficient emotional biomarkers should first be identified. In the current study, we evaluated the ability of various features to discriminate between emotions expressed during music performance with the aim of developing a BCMI system. EEG data was recorded while subjects performed simple piano music with contrasting emotional cues and rated their success in communicating the intended emotion. Power spectra and connectivity features (Magnitude Square Coherence (MSC) and Granger Causality (GC)) were extracted from the signals. Two different approaches of feature selection were used to assess the contribution of neutral baselines in detection accuracies; 1- utilizing the baselines to normalize the features, 2- not taking them into account (non-normalized features). Finally, the Support Vector Machine (SVM) has been used to evaluate and compare the capability of various features for emotion detection. Best detection accuracies were obtained from the non-normalized MSC-based features equal to 85.57 ± 2.34, 84.93 ± 1.67, and 87.16 ± 0.55 for arousal, valence, and emotional conditions respectively, while the power-based features had the lowest accuracies. Both connectivity features show acceptable accuracy while requiring short processing time and thus are potential candidates for the development of a real-time BCMI system.}, }
@article {pmid36850667, year = {2023}, author = {Siribunyaphat, N and Punsawad, Y}, title = {Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {4}, pages = {}, pmid = {36850667}, issn = {1424-8220}, abstract = {Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.}, }
@article {pmid36850530, year = {2023}, author = {Xie, Y and Oniga, S}, title = {Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {4}, pages = {}, pmid = {36850530}, issn = {1424-8220}, abstract = {In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time-frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.}, }
@article {pmid36848679, year = {2023}, author = {Letner, JG and Patel, PR and Hsieh, JC and Smith Flores, IM and Della Valle, E and Walker, LA and Weiland, JD and Chestek, CA and Cai, D}, title = {Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbf78}, pmid = {36848679}, issn = {1741-2552}, abstract = {OBJECTIVE: Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.<br><br>APPROACH: Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8 &#xb5;m diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12 weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50 &#xb5;m radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.<br><br>MAIN RESULTS: Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7&#xb1;4.6 &#xb5;m, X&#x304;&#xb1;S) in layer V motor cortex.<br><br>SIGNIFICANCE: Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.}, }
@article {pmid36848586, year = {2023}, author = {Gupta, A and Daniel, R and Rao, A and Roy, PP and Chandra, S and Kim, BG}, title = {Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.}, journal = {Big data}, volume = {}, number = {}, pages = {}, doi = {10.1089/big.2021.0204}, pmid = {36848586}, issn = {2167-647X}, abstract = {With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.}, }
@article {pmid36847833, year = {2023}, author = {Yang, Y and Garringer, HJ and Shi, Y and Lövestam, S and Peak-Chew, S and Zhang, X and Kotecha, A and Bacioglu, M and Koto, A and Takao, M and Spillantini, MG and Ghetti, B and Vidal, R and Murzin, AG and Scheres, SHW and Goedert, M}, title = {New SNCA mutation and structures of α-synuclein filaments from juvenile-onset synucleinopathy.}, journal = {Acta neuropathologica}, volume = {}, number = {}, pages = {}, pmid = {36847833}, issn = {1432-0533}, support = {MC_UP_A025-1013/MRC_/Medical Research Council/United Kingdom ; MC_U105184291/MRC_/Medical Research Council/United Kingdom ; }, abstract = {A 21-nucleotide duplication in one allele of SNCA was identified in a previously described disease with abundant α-synuclein inclusions that we now call juvenile-onset synucleinopathy (JOS). This mutation translates into the insertion of MAAAEKT after residue 22 of α-synuclein, resulting in a protein of 147 amino acids. Both wild-type and mutant proteins were present in sarkosyl-insoluble material that was extracted from frontal cortex of the individual with JOS and examined by electron cryo-microscopy. The structures of JOS filaments, comprising either a single protofilament, or a pair of protofilaments, revealed a new α-synuclein fold that differs from the folds of Lewy body diseases and multiple system atrophy (MSA). The JOS fold consists of a compact core, the sequence of which (residues 36-100 of wild-type α-synuclein) is unaffected by the mutation, and two disconnected density islands (A and B) of mixed sequences. There is a non-proteinaceous cofactor bound between the core and island A. The JOS fold resembles the common substructure of MSA Type I and Type II dimeric filaments, with its core segment approximating the C-terminal body of MSA protofilaments B and its islands mimicking the N-terminal arm of MSA protofilaments A. The partial similarity of JOS and MSA folds extends to the locations of their cofactor-binding sites. In vitro assembly of recombinant wild-type α-synuclein, its insertion mutant and their mixture yielded structures that were distinct from those of JOS filaments. Our findings provide insight into a possible mechanism of JOS fibrillation in which mutant α-synuclein of 147 amino acids forms a nucleus with the JOS fold, around which wild-type and mutant proteins assemble during elongation.}, }
@article {pmid36845071, year = {2023}, author = {Chen, D and Liu, K and Guo, J and Bi, L and Xiang, J}, title = {Editorial: Brain-computer interface and its applications.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1140508}, doi = {10.3389/fnbot.2023.1140508}, pmid = {36845071}, issn = {1662-5218}, }
@article {pmid36844572, year = {2023}, author = {Chen, PW and Ji, DH and Zhang, YS and Lee, C and Yeh, MY}, title = {Electroactive and Stretchable Hydrogels of 3,4-Ethylenedioxythiophene/thiophene Copolymers.}, journal = {ACS omega}, volume = {8}, number = {7}, pages = {6753-6761}, pmid = {36844572}, issn = {2470-1343}, abstract = {Hydrogels are conductive and stretchable, allowing for their use in flexible electronic devices, such as electronic skins, sensors, human motion monitoring, brain-computer interface, and so on. Herein, we synthesized the copolymers having various molar ratios of 3,4-ethylenedioxythiophene (EDOT) to thiophene (Th), which served as conductive additives. With doping engineering and incorporation with P(EDOT-co-Th) copolymers, hydrogels have presented excellent physical/chemical/electrical properties. It was found that the mechanical strength, adhesion ability, and conductivity of hydrogels were highly dependent on the molar ratio of EDOT to Th of the copolymers. The more the EDOT, the stronger the tensile strength and the greater the conductivity, but the lower the elongation break tends to be. By comprehensively evaluating the physical/chemical/electrical properties and cost of material use, the hydrogel incorporated with a 7:3 molar ratio P(EDOT-co-Th) copolymer was an optimal formulation for soft electronic devices.}, }
@article {pmid36844419, year = {2022}, author = {Song, M and Huang, Y and Shen, Y and Shi, C and Breeschoten, A and Konijnenburg, M and Visser, H and Romme, J and Dutta, B and Alavi, MS and Bachmann, C and Liu, YH}, title = {A 1.66Gb/s and 5.8pJ/b Transcutaneous IR-UWB Telemetry System with Hybrid Impulse Modulation for Intracortical Brain-Computer Interfaces.}, journal = {Digest of technical papers. IEEE International Solid-State Circuits Conference}, volume = {2022}, number = {}, pages = {394-396}, pmid = {36844419}, issn = {0193-6530}, }
@article {pmid36843389, year = {2023}, author = {Branco, MP and Geukes, SH and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ}, title = {Nine decades of electrocorticography: a comparison between epidural and subdural recordings.}, journal = {The European journal of neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1111/ejn.15941}, pmid = {36843389}, issn = {1460-9568}, abstract = {In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact, but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.}, }
@article {pmid36842495, year = {2023}, author = {Hua, SS and Ding, JJ and Sun, TC and Guo, C and Zhang, Y and Yu, ZH and Cao, YQ and Zhong, LH and Wu, Y and Guo, LY and Luo, JH and Cui, YH and Qiu, S}, title = {NMDAR-dependent synaptic potentiation via APPL1 signaling is required for the accessibility of a prefrontal neuronal assembly in retrieving fear extinction.}, journal = {Biological psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.biopsych.2023.02.013}, pmid = {36842495}, issn = {1873-2402}, abstract = {BACKGROUND: The ventromedial prefrontal cortex (vmPFC) has been viewed as a locus to store and recall extinction memory. However, the synaptic and cellular mechanisms underlying this process remain elusive.<br><br>METHODS: We combined transgenic mice, electrophysiological recording, activity-dependent cell labeling, and chemogenetic manipulation to analyze the role of adaptor protein APPL1 in the vmPFC for fear extinction retrieval.<br><br>RESULTS: We found that both constitutive and conditional APPL1 knockout decreases NMDA receptor (NMDAR) function in the vmPFC and impairs fear extinction retrieval. Moreover, APPL1 undergoes nuclear translocation during extinction retrieval. Blocking APPL1 nucleocytoplasmic translocation reduces NMDAR currents and disrupts extinction retrieval. We further identified a prefrontal neuronal ensemble that is both necessary and sufficient for the storage of extinction memory. Inducible APPL1 knockout in this ensemble abolishes NMDAR-dependent synaptic potentiation and disrupts extinction retrieval, while simultaneously chemogenetic activation of this ensemble rescues the impaired behaviors.<br><br>CONCLUSIONS: Therefore, our results indicate that a prefrontal neuronal ensemble stores extinction memory, and APPL1 signaling supports these neurons to retrieve extinction memory via controlling NMDAR-dependent potentiation.}, }
@article {pmid36842221, year = {2022}, author = {Liu, Z and Wang, L and Xu, S and Lu, K}, title = {A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification.}, journal = {Computers in biology and medicine}, volume = {155}, number = {}, pages = {106196}, doi = {10.1016/j.compbiomed.2022.106196}, pmid = {36842221}, issn = {1879-0534}, abstract = {Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on electroencephalography (EEG) recognition with high accuracy. However, modeling and classification of MI EEG signals remains a challenging task due to the non-linear and non-stationary characteristics of the signals. In this paper, a new time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm is proposed for the characterization and classification of MI EEG signals. Firstly, the time-varying coefficients of the time-varying autoregressive (TVAR) model are precisely approximated with the multiwavelet basis functions. Then a powerful ROFR algorithm is employed to dramatically alleviate the redundant model structure and accurately recover the relevant time-varying model parameters to obtain high resolution power spectral density (PSD) features. Finally, the features are sent to different classifiers for the classification task. To effectively improve the accuracy of classification, a principal component analysis (PCA) algorithm is utilized to determine the best feature subset and Bayesian optimization algorithm is performed to obtain the optimal parameters of the classifier. The proposed method achieves satisfactory classification accuracy on the public BCI Competition II Dataset III, which proves that this method potentially improves the recognition accuracy of MI EEG signals, and has great significance for the construction of BCI system based on MI.}, }
@article {pmid36839377, year = {2023}, author = {De Rubis, G and Paudel, KR and Manandhar, B and Singh, SK and Gupta, G and Malik, R and Shen, J and Chami, A and MacLoughlin, R and Chellappan, DK and Oliver, BGG and Hansbro, PM and Dua, K}, title = {Agarwood Oil Nanoemulsion Attenuates Cigarette Smoke-Induced Inflammation and Oxidative Stress Markers in BCi-NS1.1 Airway Epithelial Cells.}, journal = {Nutrients}, volume = {15}, number = {4}, pages = {}, pmid = {36839377}, issn = {2072-6643}, abstract = {Chronic obstructive pulmonary disease (COPD) is an irreversible inflammatory respiratory disease characterized by frequent exacerbations and symptoms such as cough and wheezing that lead to irreversible airway damage and hyperresponsiveness. The primary risk factor for COPD is chronic cigarette smoke exposure, which promotes oxidative stress and a general pro-inflammatory condition by stimulating pro-oxidant and pro-inflammatory pathways and, simultaneously, inactivating anti-inflammatory and antioxidant detoxification pathways. These events cause progressive damage resulting in impaired cell function and disease progression. Treatments available for COPD are generally aimed at reducing the symptoms of exacerbation. Failure to regulate oxidative stress and inflammation results in lung damage. In the quest for innovative treatment strategies, phytochemicals, and complex plant extracts such as agarwood essential oil are promising sources of molecules with antioxidant and anti-inflammatory activity. However, their clinical use is limited by issues such as low solubility and poor pharmacokinetic properties. These can be overcome by encapsulating the therapeutic molecules using advanced drug delivery systems such as polymeric nanosystems and nanoemulsions. In this study, agarwood oil nanoemulsion (agarwood-NE) was formulated and tested for its antioxidant and anti-inflammatory potential in cigarette smoke extract (CSE)-treated BCi-NS1.1 airway basal epithelial cells. The findings suggest successful counteractivity of agarwood-NE against CSE-mediated pro-inflammatory effects by reducing the expression of the pro-inflammatory cytokines IL-1α, IL-1β, IL-8, and GDF-15. In addition, agarwood-NE induced the expression of the anti-inflammatory mediators IL-10, IL-18BP, TFF3, GH, VDBP, relaxin-2, IFN-γ, and PDGF. Furthermore, agarwood-NE also induced the expression of antioxidant genes such as GCLC and GSTP1, simultaneously activating the PI3K pro-survival signalling pathway. This study provides proof of the dual anti-inflammatory and antioxidant activity of agarwood-NE, highlighting its enormous potential for COPD treatment.}, }
@article {pmid36822385, year = {2023}, author = {Zhang, Y and Zou, J and Ding, N}, title = {Acoustic correlates of the syllabic rhythm of speech: Modulation spectrum or local features of the temporal envelope.}, journal = {Neuroscience and biobehavioral reviews}, volume = {147}, number = {}, pages = {105111}, doi = {10.1016/j.neubiorev.2023.105111}, pmid = {36822385}, issn = {1873-7528}, abstract = {The syllable is a perceptually salient unit in speech. Since both the syllable and its acoustic correlate, i.e., the speech envelope, have a preferred range of rhythmicity between 4 and 8 Hz, it is hypothesized that theta-band neural oscillations play a major role in extracting syllables based on the envelope. A literature survey, however, reveals inconsistent evidence about the relationship between speech envelope and syllables, and the current study revisits this question by analyzing large speech corpora. It is shown that the center frequency of speech envelope, characterized by the modulation spectrum, reliably correlates with the rate of syllables only when the analysis is pooled over minutes of speech recordings. In contrast, in the time domain, a component of the speech envelope is reliably phase-locked to syllable onsets. Based on a speaker-independent model, the timing of syllable onsets explains about 24% variance of the speech envelope. These results indicate that local features in the speech envelope, instead of the modulation spectrum, are a more reliable acoustic correlate of syllables.}, }
@article {pmid36837864, year = {2023}, author = {Lin, A and Wang, T and Li, C and Pu, F and Abdelrahman, Z and Jin, M and Yang, Z and Zhang, L and Cao, X and Sun, K and Hou, T and Liu, Z and Chen, L and Chen, Z}, title = {Association of Sarcopenia with Cognitive Function and Dementia Risk Score: A National Prospective Cohort Study.}, journal = {Metabolites}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/metabo13020245}, pmid = {36837864}, issn = {2218-1989}, abstract = {The relationship between skeletal muscle and cognitive disorders has drawn increasing attention. This study aims to examine the associations of sarcopenia with cognitive function and dementia risk score. Data on 1978 participants (aged 65 years and older) from the 2011 wave of the China Health and Retirement Longitudinal Study, with four follow-up waves to 2018, were used. Cognitive function was assessed by four dimensions, with a lower score indicating lower cognitive function. Dementia risk was assessed by a risk score using the Rotterdam Study Basic Dementia Risk Model (BDRM), with a higher score indicating a greater risk. Sarcopenia was defined when low muscle mass plus low muscle strength or low physical performance were met. We used generalized estimating equations to examine the associations of sarcopenia. In the fully adjusted models, sarcopenia was significantly associated with lower cognitive function (standardized, β = -0.15; 95% CIs: -0.26, -0.04) and a higher BDRM score (standardized, β = 0.42; 95% CIs: 0.29, 0.55). Our findings may provide a new avenue for alleviating the burden of cognitive disorders by preventing sarcopenia.}, }
@article {pmid36837157, year = {2023}, author = {Liu, Q and Deng, WY and Zhang, LY and Liu, CX and Jie, WW and Su, RX and Zhou, B and Lu, LM and Liu, SW and Huang, XG}, title = {Modified Bamboo Charcoal as a Bifunctional Material for Methylene Blue Removal.}, journal = {Materials (Basel, Switzerland)}, volume = {16}, number = {4}, pages = {}, doi = {10.3390/ma16041528}, pmid = {36837157}, issn = {1996-1944}, abstract = {Biomass-derived raw bamboo charcoal (BC), NaOH-impregnated bamboo charcoal (BC-I), and magnetic bamboo charcoal (BC-IM) were fabricated and used as bio-adsorbents and Fenton-like catalysts for methylene blue removal. Compared to the raw biochar, a simple NaOH impregnation process significantly optimized the crystal structure, pore size distribution, and surface functional groups and increase the specific surface area from 1.4 to 63.0 m[2]/g. Further magnetization of the BC-I sample not only enhanced the surface area to 84.7 m[2]/g, but also improved the recycling convenience due to the superparamagnetism. The maximum adsorption capacity of BC, BC-I, and BC-IM for methylene blue at 328 K was 135.13, 220.26 and 497.51 mg/g, respectively. The pseudo-first-order rate constants k at 308 K for BC, BC-I, and BC-IM catalytic degradation in the presence of H2O2 were 0.198, 0.351, and 1.542 h[-1], respectively. A synergistic mechanism between adsorption and radical processes was proposed.}, }
@article {pmid36836747, year = {2023}, author = {Omejc, N and Peskar, M and Miladinović, A and Kavcic, V and Džeroski, S and Marusic, U}, title = {On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features.}, journal = {Life (Basel, Switzerland)}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/life13020391}, pmid = {36836747}, issn = {2075-1729}, abstract = {The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain-computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals' performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.}, }
@article {pmid36836659, year = {2023}, author = {Vasilyev, AN and Yashin, AS and Shishkin, SL}, title = {Quasi-Movements and "Quasi-Quasi-Movements": Does Residual Muscle Activation Matter?.}, journal = {Life (Basel, Switzerland)}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/life13020303}, pmid = {36836659}, issn = {2075-1729}, abstract = {Quasi-movements (QM) are observed when an individual minimizes a movement to an extent that no related muscle activation is detected. Likewise to imaginary movements (IM) and overt movements, QMs are accompanied by the event-related desynchronization (ERD) of EEG sensorimotor rhythms. Stronger ERD was observed under QMs compared to IMs in some studies. However, the difference could be caused by the remaining muscle activation in QMs that could escape detection. Here, we re-examined the relation between the electromyography (EMG) signal and ERD in QM using sensitive data analysis procedures. More trials with signs of muscle activation were observed in QMs compared with a visual task and IMs. However, the rate of such trials was not correlated with subjective estimates of actual movement. Contralateral ERD did not depend on the EMG but still was stronger in QMs compared with IMs. These results suggest that brain mechanisms are common for QMs in the strict sense and "quasi-quasi-movements" (attempts to perform the same task accompanied by detectable EMG elevation) but differ between them and IMs. QMs could be helpful in research aimed at better understanding motor action and at modeling the use of attempted movements in the brain-computer interfaces with healthy participants.}, }
@article {pmid36831864, year = {2023}, author = {Lakshminarayanan, K and Ramu, V and Rajendran, J and Chandrasekaran, KP and Shah, R and Daulat, SR and Moodley, V and Madathil, D}, title = {The Effect of Tactile Imagery Training on Reaction Time in Healthy Participants.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020321}, pmid = {36831864}, issn = {2076-3425}, abstract = {BACKGROUND: Reaction time is an important measure of sensorimotor performance and coordination and has been shown to improve with training. Various training methods have been employed in the past to improve reaction time. Tactile imagery (TI) is a method of mentally simulating a tactile sensation and has been used in brain-computer interface applications. However, it is yet unknown whether TI can have a learning effect and improve reaction time.<br><br>OBJECTIVE: The purpose of this study was to investigate the effect of TI on reaction time in healthy participants.<br><br>METHODS: We examined the reaction time to vibratory stimuli before and after a TI training session in an experimental group and compared the change in reaction time post-training with pre-training in the experimental group as well as the reaction time in a control group. A follow-up evaluation of reaction time was also conducted.<br><br>RESULTS: The results showed that TI training significantly improved reaction time after TI compared with before TI by approximately 25% (pre-TI right-hand mean &#xb1; SD: 456.62 &#xb1; 124.26 ms, pre-TI left-hand mean &#xb1; SD: 448.82 &#xb1; 124.50 ms, post-TI right-hand mean &#xb1; SD: 340.32 &#xb1; 65.59 ms, post-TI left-hand mean &#xb1; SD: 335.52 &#xb1; 59.01 ms). Furthermore, post-training reaction time showed significant reduction compared with the control group and the improved reaction time had a lasting effect even after four weeks post-training.<br><br>CONCLUSION: These findings indicate that TI training may serve as an alternate imagery strategy for improving reaction time without the need for physical practice.}, }
@article {pmid36831857, year = {2023}, author = {Peketi, S and Dhok, SB}, title = {Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020315}, pmid = {36831857}, issn = {2076-3425}, abstract = {Joint attention skills deficiency in Autism spectrum disorder (ASD) hinders individuals from communicating effectively. The P300 Electroencephalogram (EEG) signal-based brain-computer interface (BCI) helps these individuals in neurorehabilitation training to overcome this deficiency. The detection of the P300 signal is more challenging in ASD as it is noisy, has less amplitude, and has a higher latency than in other individuals. This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal identification. The EEG signal is decomposed into five modes using VMD. Thirty linear and non-linear time and frequency domain features are extracted for each mode. Synthetic minority oversampling technique data augmentation is performed to overcome the class imbalance problem in the chosen dataset. Then, a comparative analysis of three popular machine learning classifiers is performed for this application. VMD's fifth mode with a support vector machine (fine Gaussian kernel) classifier gave the best performance parameters, namely accuracy, F1-score, and the area under the curve, as 91.12%, 91.18%, and 96.6%, respectively. These results are better when compared to other state-of-the-art methods.}, }
@article {pmid36831846, year = {2023}, author = {Cattan, GH and Quemy, A}, title = {Case-Based and Quantum Classification for ERP-Based Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020303}, pmid = {36831846}, issn = {2076-3425}, abstract = {Low transfer rates are a major bottleneck for brain-computer interfaces based on electroencephalography (EEG). This problem has led to the development of more robust and accurate classifiers. In this study, we investigated the performance of variational quantum, quantum-enhanced support vector, and hypergraph case-based reasoning classifiers in the binary classification of EEG data from a P300 experiment. On the one hand, quantum classification is a promising technology to reduce computational time and improve learning outcomes. On the other hand, case-based reasoning has an excellent potential to simplify the preprocessing steps of EEG analysis. We found that the balanced training (prediction) accuracy of each of these three classifiers was 56.95 (51.83), 83.17 (50.25), and 71.10% (52.04%), respectively. In addition, case-based reasoning performed significantly lower with a simplified (49.78%) preprocessing pipeline. These results demonstrated that all classifiers were able to learn from the data and that quantum classification of EEG data was implementable; however, more research is required to enable a greater prediction accuracy because none of the classifiers were able to generalize from the data. This could be achieved by improving the configuration of the quantum classifiers (e.g., increasing the number of shots) and increasing the number of trials for hypergraph case-based reasoning classifiers through transfer learning.}, }
@article {pmid36831811, year = {2023}, author = {Liang, X and Liu, Y and Yu, Y and Liu, K and Liu, Y and Zhou, Z}, title = {Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020268}, pmid = {36831811}, issn = {2076-3425}, abstract = {Convolutional neural networks (CNNs) have shown great potential in the field of brain-computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram (EEG) signals without artificial feature extraction. Some CNNs have achieved better classification accuracy than that of traditional methods. Raw EEG signals are usually represented as a two-dimensional (2-D) matrix composed of channels and time points, ignoring the spatial topological information of electrodes. Our goal is to make a CNN that takes raw EEG signals as inputs have the ability to learn spatial topological features and improve its classification performance while basically maintaining its original structure. We propose an EEG topographic representation module (TRM). This module consists of (1) a mapping block from raw EEG signals to a 3-D topographic map and (2) a convolution block from the topographic map to an output with the same size as the input. According to the size of the convolutional kernel used in the convolution block, we design two types of TRMs, namely TRM-(5,5) and TRM-(3,3). We embed the two TRM types into three widely used CNNs (ShallowConvNet, DeepConvNet and EEGNet) and test them on two publicly available datasets (the Emergency Braking During Simulated Driving Dataset (EBDSDD) and the High Gamma Dataset (HGD)). Results show that the classification accuracies of all three CNNs are improved on both datasets after using the TRMs. With TRM-(5,5), the average classification accuracies of DeepConvNet, EEGNet and ShallowConvNet are improved by 6.54%, 1.72% and 2.07% on the EBDSDD and by 6.05%, 3.02% and 5.14% on the HGD, respectively; with TRM-(3,3), they are improved by 7.76%, 1.71% and 2.17% on the EBDSDD and by 7.61%, 5.06% and 6.28% on the HGD, respectively. We improve the classification performance of three CNNs on both datasets through the use of TRMs, indicating that they have the capability to mine spatial topological EEG information. More importantly, since the output of a TRM has the same size as the input, CNNs with raw EEG signals as inputs can use this module without changing their original structures.}, }
@article {pmid36831784, year = {2023}, author = {Arı, E and Taçgın, E}, title = {Input Shape Effect on Classification Performance of Raw EEG Motor Imagery Signals with Convolutional Neural Networks for Use in Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020240}, pmid = {36831784}, issn = {2076-3425}, abstract = {EEG signals are interpreted, analyzed and classified by many researchers for use in brain-computer interfaces. Although there are many different EEG signal acquisition methods, one of the most interesting is motor imagery signals. Many different signal processing methods, machine learning and deep learning models have been developed for the classification of motor imagery signals. Among these, Convolutional Neural Network models generally achieve better results than other models. Because the size and shape of the data is important for training Convolutional Neural Network models and discovering the right relationships, researchers have designed and experimented with many different input shape structures. However, no study has been found in the literature evaluating the effect of different input shapes on model performance and accuracy. In this study, the effects of different input shapes on model performance and accuracy in the classification of EEG motor imagery signals were investigated, which had not been specifically studied before. In addition, signal preprocessing methods, which take a long time before classification, were not used; rather, two CNN models were developed for training and classification using raw data. Two different datasets, BCI Competition IV 2A and 2B, were used in classification processes. For different input shapes, 53.03-89.29% classification accuracy and 2-23 s epoch time were obtained for 2A dataset, 64.84-84.94% classification accuracy and 4-10 s epoch time were obtained for 2B dataset. This study showed that the input shape has a significant effect on the classification performance, and when the correct input shape is selected and the correct CNN architecture is developed, feature extraction and classification can be done well by the CNN architecture without any signal preprocessing.}, }
@article {pmid36831764, year = {2023}, author = {Hu, H and Yue, K and Guo, M and Lu, K and Liu, Y}, title = {Subject Separation Network for Reducing Calibration Time of MI-Based BCI.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020221}, pmid = {36831764}, issn = {2076-3425}, abstract = {Motor imagery brain-computer interface (MI-based BCIs) have demonstrated great potential in various applications. However, to well generalize classifiers to new subjects, a time-consuming calibration process is necessary due to high inter-subject variabilities of EEG signals. This process is costly and tedious, hindering the further expansion of MI-based BCIs outside of the laboratory. To reduce the calibration time of MI-based BCIs, we propose a novel domain adaptation framework that adapts multiple source subjects' labeled data to the unseen trials of target subjects. Firstly, we train one Subject Separation Network(SSN) for each of the source subjects in the dataset. Based on adversarial domain adaptation, a shared encoder is constructed to learn similar representations for both domains. Secondly, to model the factors that cause subject variabilities and eliminate the correlated noise existing in common feature space, private feature spaces orthogonal to the shared counterpart are learned for each subject. We use a shared decoder to validate that the model is actually learning from task-relevant neurophysiological information. At last, an ensemble classifier is built by the integration of the SSNs using the information extracted from each subject's task-relevant characteristics. To quantify the efficacy of the framework, we analyze the accuracy-calibration cost trade-off in MI-based BCIs, and theoretically guarantee a generalization bound on the target error. Visualizations of the transformed features illustrate the effectiveness of domain adaptation. The experimental results on the BCI Competition IV-IIa dataset prove the effectiveness of the proposed framework compared with multiple classification methods. We infer from our results that users could learn to control MI-based BCIs without a heavy calibration process. Our study further shows how to design and train Neural Networks to decode task-related information from different subjects and highlights the potential of deep learning methods for inter-subject EEG decoding.}, }
@article {pmid36829694, year = {2023}, author = {Nawaz, R and Wood, G and Nisar, H and Yap, VV}, title = {Exploring the Effects of EEG-Based Alpha Neurofeedback on Working Memory Capacity in Healthy Participants.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {2}, pages = {}, doi = {10.3390/bioengineering10020200}, pmid = {36829694}, issn = {2306-5354}, abstract = {Neurofeedback, an operant conditioning neuromodulation technique, uses information from brain activities in real-time via brain-computer interface (BCI) technology. This technique has been utilized to enhance the cognitive abilities, including working memory performance, of human beings. The aims of this study are to investigate how alpha neurofeedback can improve working memory performance in healthy participants and to explore the underlying neural mechanisms in a working memory task before and after neurofeedback. Thirty-six participants divided into the NFT group and the control group participated in this study. This study was not blinded, and both the participants and the researcher were aware of their group assignments. Increasing power in the alpha EEG band was used as a neurofeedback in the eyes-open condition only in the NFT group. The data were collected before and after neurofeedback while they were performing the N-back memory task (N = 1 and N = 2). Both groups showed improvement in their working memory performance. There was an enhancement in the power of their frontal alpha and beta activities with increased working memory load (i.e., 2-back). The experimental group showed improvements in their functional connections between different brain regions at the theta level. This effect was absent in the control group. Furthermore, brain hemispheric lateralization was found during the N-back task, and there were more intra-hemisphere connections than inter-hemisphere connections of the brain. These results suggest that healthy participants can benefit from neurofeedback and from having their brain networks changed after the training.}, }
@article {pmid36829681, year = {2023}, author = {Lee, PL and Chen, SH and Chang, TC and Lee, WK and Hsu, HT and Chang, HH}, title = {Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {2}, pages = {}, doi = {10.3390/bioengineering10020186}, pmid = {36829681}, issn = {2306-5354}, abstract = {The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user's MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.}, }
@article {pmid36827704, year = {2023}, author = {Ming, G and Zhong, H and Pei, W and Gao, X and Wang, Y}, title = {A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbee0}, pmid = {36827704}, issn = {1741-2552}, abstract = {Objective.The traditional uniform flickering stimulation pattern shows strong steady-state visual evoked potential (SSVEP) responses and poor user experience with intense flicker perception. To achieve a balance between performance and comfort in SSVEP-based brain-computer interface (BCI) systems, this study proposed a new grid stimulation pattern with reduced stimulation area and low spatial contrast.Approach.A spatial contrast scanning experiment was conducted first to clarify the relationship between the SSVEP characteristics and the signs and values of spatial contrast. Four stimulation patterns were involved in the experiment: the ON and OFF grid stimulation patterns that separately activated the positive or negative contrast information processing pathways, the ON-OFF grid stimulation pattern that simultaneously activated both pathways, and the uniform flickering stimulation pattern that served as a control group. The contrast-intensity and contrast-user experience curves were obtained for each stimulation pattern. Accordingly, the optimized stimulation schemes with low spatial contrast (the ON-50% grid stimulus, the OFF-50% grid stimulus, and the Flicker-30% stimulus) were applied in a 12-target and a 40-target BCI speller and compared with the traditional uniform flickering stimulus (the Flicker-500% stimulus) in the evaluation of BCI performance and subjective experience.Main results.The OFF-50% grid stimulus showed comparable online performance (12-target, 2 s: 69.87±0.74 vs. 69.76±0.58 bits min[-1], 40-target, 4 s: 57.02±2.53 vs. 60.79±1.08 bits min[-1]) and improved user experience (better comfortable level, weaker flicker perception and higher preference level) compared to the traditional Flicker-500% stimulus in both multi-targets BCI spellers.Significance.Selective activation of the negative contrast information processing pathway using the new OFF-50% grid stimulus evoked robust SSVEP responses. On this basis, high-performance and user-friendly SSVEP-based BCIs have been developed and implemented, which has important theoretical significance and application value in promoting the development of the visual BCI technology.}, }
@article {pmid36827271, year = {2023}, author = {McCaffrey, KR and Balaguera-Reina, SA and Falk, BG and Gati, EV and Cole, JM and Mazzotti, FJ}, title = {How to estimate body condition in large lizards? Argentine black and white tegu (Salvator merianae, Duméril and Bibron, 1839) as a case study.}, journal = {PloS one}, volume = {18}, number = {2}, pages = {e0282093}, doi = {10.1371/journal.pone.0282093}, pmid = {36827271}, issn = {1932-6203}, abstract = {Body condition is a measure of the health and fitness of an organism represented by available energy stores, typically fat. Direct measurements of fat are difficult to obtain non-invasively, thus body condition is usually estimated by calculating body condition indices (BCIs) using mass and length. The utility of BCIs is contingent on the relationship of BCIs and fat, thereby validation studies should be performed to select the best performing BCI before application in ecological investigations. We evaluated 11 BCIs in 883 Argentine black and white tegus (Salvator merianae) removed from their non-native range in South Florida, United States. Because the length-mass relationship in tegus is allometric, a segmented linear regression model was fit to the relationship between mass and length to define size classes. We evaluated percent, residual, and scaled fat and determined percent fat was the best measure of fat, because it was the least-associated with snout-vent length (SVL). We evaluated performance of BCIs with the full dataset and within size classes and identified Fulton's K as the best performing BCI for our sampled population, explaining up to 19% of the variation in fat content. Overall, we found that BCIs: 1) maintained relatively weak relationships with measures of fat and 2) splitting data into size classes reduced the strength of the relationship (i.e., bias) between percent fat and SVL but did not improve the performance of BCIs. We postulate that the weak performance of BCIs in our dataset was likely due to the weak association of fat with SVL, the body plan and life-history traits of tegus, and potentially inadequate accounting of available energy resources. We caution against assuming that BCIs are strong indicators of body condition across species and suggest that validation studies be implemented, or that alternative or complimentary measures of health or fitness should be considered.}, }
@article {pmid36825130, year = {2023}, author = {Jatupornpoonsub, T and Thimachai, P and Supasyndh, O and Wongsawat, Y}, title = {QEEG characteristics associated with malnutrition-inflammation complex syndrome.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {944988}, doi = {10.3389/fnhum.2023.944988}, pmid = {36825130}, issn = {1662-5161}, abstract = {End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.}, }
@article {pmid36825118, year = {2023}, author = {Wang, X and Xing, K and He, M and He, T and Xiang, X and Chen, T and Zhang, L and Li, H}, title = {Time-restricted feeding is an intervention against excessive dark-phase sleepiness induced by obesogenic diet.}, journal = {National science review}, volume = {10}, number = {1}, pages = {nwac222}, doi = {10.1093/nsr/nwac222}, pmid = {36825118}, issn = {2053-714X}, abstract = {High-fat diet (HFD)-induced obesity is a growing epidemic and major health concern. While excessive daytime sleepiness (EDS) is a common symptom of HFD-induced obesity, preliminary findings suggest that reduced wakefulness could be improved with time-restricted feeding (TRF). At present, however, the underlying neural mechanisms remain largely unknown. The paraventricular thalamic nucleus (PVT) plays a role in maintaining wakefulness. We found that chronic HFD impaired the activity of PVT neurons. Notably, inactivation of the PVT was sufficient to reduce and fragment wakefulness during the active phase in lean mice, similar to the sleep-wake alterations observed in obese mice with HFD-induced obesity. On the other hand, enhancing PVT neuronal activity consolidated wakefulness in mice with HFD-induced obesity. We observed that the fragmented wakefulness could be eliminated and reversed by TRF. Furthermore, TRF prevented the HFD-induced disruptions on synaptic transmission in the PVT, in a feeding duration-dependent manner. Collectively, our findings demonstrate that ad libitum access to a HFD results in inactivation of the PVT, which is critical to impaired nocturnal wakefulness and increased sleep, while TRF can prevent and reverse diet-induced PVT dysfunction and excessive sleepiness. We establish a link between TRF and neural activity, through which TRF can potentially serve as a lifestyle intervention against diet/obesity-related EDS.}, }
@article {pmid36824667, year = {2023}, author = {Chen, M and Chen, Z and Xiao, X and Zhou, L and Fu, R and Jiang, X and Pang, M and Xia, J}, title = {Corticospinal circuit neuroplasticity may involve silent synapses: Implications for functional recovery facilitated by neuromodulation after spinal cord injury.}, journal = {IBRO neuroscience reports}, volume = {14}, number = {}, pages = {185-194}, doi = {10.1016/j.ibneur.2022.08.005}, pmid = {36824667}, issn = {2667-2421}, abstract = {Spinal cord injury (SCI) leads to devastating physical consequences, such as severe sensorimotor dysfunction even lifetime disability, by damaging the corticospinal system. The conventional opinion that SCI is intractable due to the poor regeneration of neurons in the adult central nervous system (CNS) needs to be revisited as the CNS is capable of considerable plasticity, which underlie recovery from neural injury. Substantial spontaneous neuroplasticity has been demonstrated in the corticospinal motor circuitry following SCI. Some of these plastic changes appear to be beneficial while others are detrimental toward locomotor function recovery after SCI. The beneficial corticospinal plasticity in the spared corticospinal circuits can be harnessed therapeutically by multiple contemporary neuromodulatory approaches, especially the electrical stimulation-based modalities, in an activity-dependent manner to improve functional outcomes in post-SCI rehabilitation. Silent synapse generation and unsilencing contribute to profound neuroplasticity that is implicated in a variety of neurological disorders, thus they may be involved in the corticospinal motor circuit neuroplasticity following SCI. Exploring the underlying mechanisms of silent synapse-mediated neuroplasticity in the corticospinal motor circuitry that may be exploited by neuromodulation will inform a novel direction for optimizing therapeutic repair strategies and rehabilitative interventions in SCI patients.}, }
@article {pmid36822277, year = {2023}, author = {Gan, A and Gong, A and Ding, P and Yuan, X and Chen, M and Fu, Y and Cheng, Y}, title = {Computer-aided diagnosis of schizophrenia based on node2vec and Transformer.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109824}, doi = {10.1016/j.jneumeth.2023.109824}, pmid = {36822277}, issn = {1872-678X}, abstract = {OBJECTIVE: Compared with the healthy control(HC) group, the brain structure and function of schizophrenia(SZ) patients are significantly abnormal, so brain imaging methods can be used to achieve the aided diagnosis of SZ. However, a brain network based on brain imaging data is non-Euclidean, and its intrinsic features cannot be learned effectively by general deep learning models. Furthermore, in the majority of existing studies, brain network features were manually specified as the input of machine learning models.<br><br>METHODS: In this study, brain functional network constructed from the subject's fMRI data is analyzed, and its small-world value is calculated and t-tested; the node2vec algorithm in graph embedding is introduced to transform the constructed brain network into low-dimensional dense vectors, and the brain network's non-Euclidean spatial structure characteristics are retained to the greatest extent, so that its intrinsic features can be extracted by deep learning models; GridMask is used to randomly mask part of the information in the vectors to enhance the data; and then features can be extracted using the Transformer model to identify SZ.<br><br>RESULTS: It is again shown that the small-world value of the brain network in SZ is significantly lower than that in HC by t-test (p=0.014&#xa1;0.05). 97.78% classification accuracy is achieved by the proposed methods (node2vec + GridMask + Transformer) in 30 SZ patients and 30 healthy people.<br><br>CONCLUSION: The experiment shows that the node2vec used in this paper can effectively solve the problem of brain network features being difficult to learn by general deep learning models. The high-precision computer-aided diagnosis of SZ can be obtained by combining node2vec with Transformer and GridMask.<br><br>SIGNIFICANCE: The proposed methods in the paper are expected to be used for aided diagnosis of SZ.}, }
@article {pmid36821640, year = {2023}, author = {Jeunet, C and N'Kaoua, B and Subramanian, S and Hachet, M and Lotte, F}, title = {Correction: Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.}, journal = {PloS one}, volume = {18}, number = {2}, pages = {e0282281}, doi = {10.1371/journal.pone.0282281}, pmid = {36821640}, issn = {1932-6203}, abstract = {[This corrects the article DOI: 10.1371/journal.pone.0143962.].}, }
@article {pmid36821578, year = {2023}, author = {Goodrich, JA and Walker, DI and He, J and Lin, X and Baumert, BO and Hu, X and Alderete, TL and Chen, Z and Valvi, D and Fuentes, ZC and Rock, S and Wang, H and Berhane, K and Gilliland, FD and Goran, MI and Jones, DP and Conti, DV and Chatzi, L}, title = {Metabolic Signatures of Youth Exposure to Mixtures of Per- and Polyfluoroalkyl Substances: A Multi-Cohort Study.}, journal = {Environmental health perspectives}, volume = {131}, number = {2}, pages = {27005}, doi = {10.1289/EHP11372}, pmid = {36821578}, issn = {1552-9924}, abstract = {BACKGROUND: Exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous and has been associated with an increased risk of several cardiometabolic diseases. However, the metabolic pathways linking PFAS exposure and human disease are unclear.<br><br>OBJECTIVE: We examined associations of PFAS mixtures with alterations in metabolic pathways in independent cohorts of adolescents and young adults.<br><br>METHODS: Three hundred twelve overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) and 137 young adults from the Southern California Children's Health Study (CHS) were included in the analysis. Plasma PFAS and the metabolome were determined using liquid-chromatography/high-resolution mass spectrometry. A metabolome-wide association study was performed on log-transformed metabolites using Bayesian regression with a g-prior specification and g-computation for modeling exposure mixtures to estimate the impact of exposure to a mixture of six ubiquitous PFAS (PFOS, PFHxS, PFHpS, PFOA, PFNA, and PFDA). Pathway enrichment analysis was performed using Mummichog and Gene Set Enrichment Analysis. Significance across cohorts was determined using weighted Z-tests.<br><br>RESULTS: In the SOLAR and CHS cohorts, PFAS exposure was associated with alterations in tyrosine metabolism (meta-analysis p=0.00002) and de novo fatty acid biosynthesis (p=0.03), among others. For example, when increasing all PFAS in the mixture from low (&#x223c;30th percentile) to high (&#x223c;70th percentile), thyroxine (T4), a thyroid hormone related to tyrosine metabolism, increased by 0.72 standard deviations (SDs; equivalent to a standardized mean difference) in the SOLAR cohort (95% Bayesian credible interval (BCI): 0.00, 1.20) and 1.60 SD in the CHS cohort (95% BCI: 0.39, 2.80). Similarly, when going from low to high PFAS exposure, arachidonic acid increased by 0.81 SD in the SOLAR cohort (95% BCI: 0.37, 1.30) and 0.67 SD in the CHS cohort (95% BCI: 0.00, 1.50). In general, no individual PFAS appeared to drive the observed associations.<br><br>DISCUSSION: Exposure to PFAS is associated with alterations in amino acid metabolism and lipid metabolism in adolescents and young adults. https://doi.org/10.1289/EHP11372.}, }
@article {pmid36821341, year = {2023}, author = {Chen, Y and Chen, S and Zhang, X and Zhang, S and Jia, K and Anderson, BA and Gong, M}, title = {Reward history modulates attention based on feature relationship.}, journal = {Journal of experimental psychology. General}, volume = {}, number = {}, pages = {}, doi = {10.1037/xge0001384}, pmid = {36821341}, issn = {1939-2222}, abstract = {Prioritizing attention to reward-predictive items is critical for survival, but challenging because these items rarely appear in the same feature or within the same environment. However, whether attention selection can be adaptively tuned to items that matched the context-dependent, relative feature of previously rewarded items remains largely unknown. In four experiments (N = 40 per experiment), we trained participants to learn the color-reward association and then adopted visual search tasks in which the color of a singleton distractor matched either the feature value (e.g., red or yellow) or feature relationship (i.e., redder or yellower) of previously rewarded colors. We consistently found enhanced attentional capture by a singleton distractor when it was relationally matched to the high reward compared with the low reward relationship, in addition to observing the typical effect of learned value on singletons matching the previously rewarded colors. Our findings provide novel evidence for the flexibility of value-driven attention via feature relationship, which is particularly useful given the changeable sensory inputs in real-world searches. (PsycInfo Database Record (c) 2023 APA, all rights reserved).}, }
@article {pmid36820790, year = {2022}, author = {Bretton-Granatoor, Z and Stealey, H and Santacruz, SR and Lewis-Peacock, JA}, title = {Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data.}, journal = {IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference}, volume = {2022}, number = {}, pages = {650-654}, pmid = {36820790}, abstract = {Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/-4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.}, }
@article {pmid36817868, year = {2023}, author = {Xiao, HY and Chai, JY and Fang, YY and Lai, YS}, title = {The spatial-temporal risk profiling of Clonorchis sinensis infection over 50 years implies the effectiveness of control programs in South Korea: a geostatistical modeling study.}, journal = {The Lancet regional health. Western Pacific}, volume = {33}, number = {}, pages = {100697}, pmid = {36817868}, issn = {2666-6065}, abstract = {BACKGROUND: Over the past 50 years, two national control programs on Clonorchis sinensis infection have been conducted in South Korea. Spatial-temporal profiles of infection risk provide useful information on assessing the effectiveness of the programs and planning spatial-targeted control strategies.<br><br>METHODS: Advanced Bayesian geostatistical joint models with spatial-temporal random effects were developed to analyze disease data collecting by a systematic review with potential influencing factors, and to handle issues of preferential sampling and data heterogeneities. Changes of the infection risk were analyzed.<br><br>FINDINGS: We presented the first spatial-temporal risk maps of C.&#xa0;sinensis infection at 5&#xa0;&#xd7;&#xa0;5&#xa0;km[2] resolution from 1970 to 2020 in South Korea. Moderate-to-high risk areas were shrunk, but temporal variances were shown in different areas. The population-adjusted estimated prevalence across the country was 5.99% (95% BCI: 5.09-7.01%) in 1970, when the first national deworming campaign began. It declined to 3.95% (95% BCI: 2.88-3.95%) in 1995, when the campaign suspended, and increased to 4.73% (95% BCI: 4.00-5.42%) in 2004, just before the Clonorchiasis Eradication Program (CEP). The population-adjusted prevalence was estimated at 2.77% (95% BCI: 1.67-4.34%) in 2020, 15 years after CEP started, corresponding to 1.42 (95% BCI: 0.85-2.23) million infected people.<br><br>INTERPRETATION: The first nationwide campaign and the CEP showed effectiveness on control of C.&#xa0;sinensis infection. Moderate-to-high risk areas identified by risk maps should be prioritized for control and intervention.<br><br>FUNDING: The National Natural Science Foundation of China (project no. 82073665) and the Natural Science Foundation of Guangdong Province (project no. 2022A1515010042).}, }
@article {pmid36817607, year = {2023}, author = {Shang, YF and Shen, YY and Zhang, MC and Lv, MC and Wang, TY and Chen, XQ and Lin, J}, title = {Progress in salivary glands: Endocrine glands with immune functions.}, journal = {Frontiers in endocrinology}, volume = {14}, number = {}, pages = {1061235}, pmid = {36817607}, issn = {1664-2392}, abstract = {The production and secretion of saliva is an essential function of the salivary glands. Saliva is a complicated liquid with different functions, including moistening, digestion, mineralization, lubrication, and mucosal protection. This review focuses on the mechanism and neural regulation of salivary secretion, and saliva is secreted in response to various stimuli, including odor, taste, vision, and mastication. The chemical and physical properties of saliva change dynamically during physiological and pathophysiological processes. Moreover, the central nervous system modulates salivary secretion and function via various neurotransmitters and neuroreceptors. Smell, vision, and taste have been investigated for the connection between salivation and brain function. The immune and endocrine functions of the salivary glands have been explored recently. Salivary glands play an essential role in innate and adaptive immunity and protection. Various immune cells such as B cells, T cells, macrophages, and dendritic cells, as well as immunoglobins like IgA and IgG have been found in salivary glands. Evidence supports the synthesis of corticosterone, testosterone, and melatonin in salivary glands. Saliva contains many potential biomarkers derived from epithelial cells, gingival crevicular fluid, and serum. High level of matrix metalloproteinases and cytokines are potential markers for oral carcinoma, infectious disease in the oral cavity, and systemic disease. Further research is required to monitor and predict potential salivary biomarkers for health and disease in clinical practice and precision medicine.}, }
@article {pmid36817318, year = {2023}, author = {Zhang, Z and Li, D and Zhao, Y and Fan, Z and Xiang, J and Wang, X and Cui, X}, title = {A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1101726}, pmid = {36817318}, issn = {1662-5188}, abstract = {INTRODUCTION: Speller is the best way to express the performance of the brain-computer interface (BCI) paradigm. Due to its advantages of short analysis time and high accuracy, the SSVEP paradigm has been widely used in the BCI speller system based on the wet electrode. It is widely known that the wet electrode operation is cumbersome and that the subjects have a poor experience. In addition, in the asynchronous SSVEP system based on threshold analysis, the system flickers continuously from the beginning to the end of the experiment, which leads to visual fatigue. The dry electrode has a simple operation and provides a comfortable experience for subjects. The EOG signal can avoid the stimulation of SSVEP for a long time, thus reducing fatigue.<br><br>METHODS: This study first designed the brain-controlled switch based on continuous blinking EOG signal and SSVEP signal to improve the flexibility of the BCI speller. Second, in order to increase the number of speller instructions, we designed the time-space frequency conversion (TSFC) SSVEP stimulus paradigm by constantly changing the time and space frequency of SSVEP sub-stimulus blocks, and designed a speller in a dry electrode environment.<br><br>RESULTS: Seven subjects participated and completed the experiments. The results showed that the accuracy of the brain-controlled switch designed in this study was up to 94.64%, and all the subjects could use the speller flexibly. The designed 60-character speller based on the TSFC-SSVEP stimulus paradigm has an accuracy rate of 90.18% and an information transmission rate (ITR) of 117.05 bits/min. All subjects can output the specified characters in a short time.<br><br>DISCUSSION: This study designed and implemented a multi-instruction SSVEP speller based on dry electrode. Through the combination of EOG and SSVEP signals, the speller can be flexibly controlled. The frequency of SSVEP stimulation sub-block is recoded in time and space by TSFC-SSVEP stimulation paradigm, which greatly improves the number of output instructions of BCI system in dry electrode environment. This work only uses FBCCA algorithm to test the stimulus paradigm, which requires a long stimulus time. In the future, we will use trained algorithms to study stimulus paradigm to improve its overall performance.}, }
@article {pmid36816135, year = {2023}, author = {Wang, M and Zhou, H and Li, X and Chen, S and Gao, D and Zhang, Y}, title = {Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1113593}, pmid = {36816135}, issn = {1662-4548}, abstract = {Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological features of the brain function network. For signal denoising and channel filtering, raw MI EEG was filtered based on an R[2] map, and then the wavelet soft threshold and one-to-one multi-class score common spatial pattern algorithms were used. Subsequently, the relative wavelet packet entropy and corresponding topological features of the brain network were extracted. After feature fusion, mutcorLasso and the relief-f method were applied for feature selection, followed by three classifiers and an ensemble classifier, respectively. The experiments were conducted on two public EEG datasets (BCI Competition III dataset IIIa and BCI Competition IV dataset IIa) to verify this proposed method. The results showed that the brain network topology features and feature selection methods can retain the information of EEG more effectively and reduce the computational complexity, and the average classification accuracy for both public datasets was above 90%; hence, this algorithms is suitable in MI-BCI and has potential applications in rehabilitation and other fields.}, }
@article {pmid36813927, year = {2023}, author = {Shang, Y and Gao, X and An, A}, title = {Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36813927}, issn = {1741-0444}, abstract = {Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.}, }
@article {pmid36812763, year = {2023}, author = {Barrier, ML and Myszor, IT and Sahariah, P and Sigurdsson, S and Carmena-Bargueño, M and Pérez-Sánchez, H and Gudmundsson, GH}, title = {Aroylated phenylenediamine HO53 modulates innate immunity, histone acetylation and metabolism.}, journal = {Molecular immunology}, volume = {155}, number = {}, pages = {153-164}, doi = {10.1016/j.molimm.2023.02.003}, pmid = {36812763}, issn = {1872-9142}, abstract = {In the current context of antibiotic resistance, the need to find alternative treatment strategies is urgent. Our research aimed to use synthetized aroylated phenylenediamines (APDs) to induce the expression of cathelicidin antimicrobial peptide gene (CAMP) to minimize the necessity of antibiotic use during infection. One of these compounds, HO53, showed promising results in inducing CAMP expression in bronchial epithelium cells (BCi-NS1.1 hereafter BCi). Thus, to decipher the cellular effects of HO53 on BCi cells, we performed RNA sequencing (RNAseq) analysis after 4, 8 and 24 h treatment of HO53. The number of differentially expressed transcripts pointed out an epigenetic modulation. Yet, the chemical structure and in silico modeling indicated HO53 as a histone deacetylase (HDAC) inhibitor. When exposed to a histone acetyl transferase (HAT) inhibitor, BCi cells showed a decreased expression of CAMP. Inversely, when treated with a specific HDAC3 inhibitor (RGFP996), BCi cells showed an increased expression of CAMP, indicating acetylation status in cells as determinant for the induction of the expression of the gene CAMP expression. Interestingly, a combination treatment with both HO53 and HDAC3 inhibitor RGFP966 leads to a further increase of CAMP expression. Moreover, HDAC3 inhibition by RGFP966 leads to increased expression of STAT3 and HIF1A, both previously demonstrated to be involved in pathways regulating CAMP expression. Importantly, HIF1α is considered as a master regulator in metabolism. A significant number of genes of metabolic enzymes were detected in our RNAseq data with enhanced expression conveying a shift toward enhanced glycolysis. Overall, we are demonstrating that HO53 might have a translational value against infections in the future through a mechanism leading to innate immunity strengthening involving HDAC inhibition and shifting the cells towards an immunometabolism, which further favors innate immunity activation.}, }
@article {pmid36808912, year = {2023}, author = {Li, R and Hu, H and Zhao, X and Wang, Z and Xu, G}, title = {A static paradigm based on illusion-induced VEP for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbdc0}, pmid = {36808912}, issn = {1741-2552}, abstract = {OBJECTIVE: Visual evoked potentials (VEPs) have been commonly applied in brain-computer interfaces (BCIs) due to their satisfactory classification performance recently. However, most existing methods with flickering or oscillating stimuli will induce visual fatigue under long-term training, thus restricting the implementation of VEP-based BCIs. To address this issue, a novel paradigm adopting static motion illusion based on illusion-induced visual evoked potential (IVEP) is proposed for BCIs to enhance visual experience and practicality.<br><br>APPROACH: This study explored the responses to baseline and illusion tasks including the Rotating-Tilted-Lines (RTL) illusion and Rotating-Snakes (RS) illusion. The distinguishable features were examined between different illusions by analyzing the event-related potentials (ERPs) and amplitude modulation of evoked oscillatory responses.<br><br>MAIN RESULTS: The illusion stimuli elicited VEPs in an early time window encompassing a negative component (N1) from 110 to 200 ms and a positive component (P2) between 210 and 300 ms. Based on the feature analysis, a filter bank was designed to extract discriminative signals. The task-related component analysis (TRCA) was used to evaluate the binary classification task performance of the proposed method. Then the highest accuracy of 86.67% was achieved with a data length of 0.6 s.<br><br>SIGNIFICANCE: The results of this study demonstrate that the static motion illusion paradigm has the feasibility of implementation and is promising for VEP-based BCI applications.}, }
@article {pmid36805270, year = {2023}, author = {Liu, X and Whalen, AJ and Ryu, SB and Lee, SW and Fried, SI and Kim, K and Cai, C and Lauritzen, M and Bertram, N and Chang, B and Yu, T and Han, A}, title = {MEMS micro-coils for magnetic neurostimulation.}, journal = {Biosensors &amp; bioelectronics}, volume = {227}, number = {}, pages = {115143}, doi = {10.1016/j.bios.2023.115143}, pmid = {36805270}, issn = {1873-4235}, abstract = {Micro-coil magnetic stimulation of brain tissue presents new challenges for MEMS micro-coil probe fabrication. The main challenges are threefold; (i) low coil resistance for high power efficiency, (ii) low leak current from the probe into the in vitro experimental set-up, (iii) adaptive MEMS process technology because of the dynamic research area, which requires agile design changes. Taking on these challenges, we present a MEMS fabrication process that has three main features; (i) multilayer resist lift-off process to pattern up to 1800-nm-thick metal films, and special care is taken to obtain high conductivity thin-films by physical vapor deposition, and (ii) all micro-coil Al wires are encapsulated in at least 200 nm of ALD alumina and 6-μm-thick parylene C such the leak resistance is high (>210 GΩ), (iii) combining a multi-step DRIE process and maskless photolithography for adaptive design and device fabrication. The entire process requires four lithography steps. Because we avoided SOI wafers and lithography mask fabrication, the design-to-device time is shortened significantly. The resulting probes are 4-mm-long, 60-μm-thick, and down to 150 μm-wide. Selected MEMS coil devices were validated in vivo using mice and compared to previous work.}, }
@article {pmid36805091, year = {2023}, author = {Zhang, Y and Wang, Y and Li, Z and Wang, Z and Cheng, J and Bai, X and Hsu, YC and Sun, Y and Li, S and Shi, J and Sui, B and Bai, R}, title = {Vascular-water-exchange MRI (VEXI) enables the detection of subtle AXR alterations in Alzheimer's disease without MRI contrast agent, which may relate to BBB integrity.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119951}, doi = {10.1016/j.neuroimage.2023.119951}, pmid = {36805091}, issn = {1095-9572}, abstract = {Blood-brain barrier (BBB) impairment is an important pathophysiological process in Alzheimer's disease (AD) and a potential biomarker for early diagnosis of AD. However, most current neuroimaging methods assessing BBB function need the injection of exogenous contrast agents (or tracers), which limits the application of these methods in a large population. In this study, we aim to explore the feasibility of vascular water exchange MRI (VEXI), a diffusion-MRI-based method proposed to assess the BBB permeability to water molecules without using a contrast agent, in the detection of the BBB breakdown in AD. We tested VEXI on a 3T MRI scanner on three groups: AD patients (AD group), mild cognitive impairment (MCI) patients due to AD (MCI group), and the age-matched normal cognition subjects (NC group). Interestingly, we find that the apparent water exchange across the BBB (AXRBBB) measured by VEXI shows higher values in MCI compared with NC, and this higher AXRBBB happens specifically in the hippocampus. This increase in AXRBBB value increase gets larger and extends to more brain regions (medial orbital frontal cortex and thalamus) from MCI group to the AD group. Furthermore, we find that the AXRBBB of these three regions detected by VEXI is correlated significantly with impairment of respective cognitive domains independent of age, sex and education. These results suggest VEXI is a promising method to assess the BBB breakdown in AD.}, }
@article {pmid36801814, year = {2023}, author = {Lakshminarayanan, K and Shah, R and Yao, Y and Madathil, D}, title = {The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery.}, journal = {Motor control}, volume = {}, number = {}, pages = {1-14}, doi = {10.1123/mc.2022-0061}, pmid = {36801814}, issn = {1087-1640}, abstract = {Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain-computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery-based brain-computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion-extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery-related event-related desynchronization and improved task classification performance.}, }
@article {pmid36801453, year = {2023}, author = {Zhao, K and Zhu, J and Yang, L and Shang, Z and Wan, H}, title = {Goal given moment modulates the time period of gamma oscillations in nidopallium caudolaterale during the goal-directed behavior of pigeon.}, journal = {Brain research}, volume = {}, number = {}, pages = {148288}, doi = {10.1016/j.brainres.2023.148288}, pmid = {36801453}, issn = {1872-6240}, abstract = {The cognitive processes of goal-directed navigation are believed to be organized around and serve the identification and selection of goals. Differences in LFP signals in avian nidopallium caudolaterale (NCL) under different goal location/distance information in the goal-directed behavior have been studied. However, for goals that are multifarious constructs that include various information, the modulation of goal time information on the LFP of NCL during goal-directed behavior remains unclear. In this study, we recorded the LFP activity from the NCL of eight pigeons as they performed two goal-directed decision-making tasks in a plus-maze. During the two tasks with different goal time information, spectral analysis revealed significant LFP power selectively enhanced in the slow gamma band (40-60 Hz), while the slow gamma band of LFP which could effectively decode the behavioral goal of the pigeons existed in different time periods. These findings suggest that the LFP activity in the gamma band correlates with the goal-time information, and help to shed light on the contribution of the gamma rhythm recorded from the NCL in goal-directed behavior.}, }
@article {pmid36801435, year = {2023}, author = {Chikhi, S and Matton, N and Sanna, M and Blanchet, S}, title = {Mental strategies and resting state EEG: effect on high alpha amplitude modulation by neurofeedback in healthy young adults.}, journal = {Biological psychology}, volume = {}, number = {}, pages = {108521}, doi = {10.1016/j.biopsycho.2023.108521}, pmid = {36801435}, issn = {1873-6246}, abstract = {Neurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants' neuromodulation ability of high alpha (10-12Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB.}, }
@article {pmid36801241, year = {2023}, author = {Gu, L and Jiang, J and Han, H and Gan, JQ and Wang, H}, title = {Recognition of unilateral lower limb movement based on EEG signals with ERP-PCA analysis.}, journal = {Neuroscience letters}, volume = {800}, number = {}, pages = {137133}, doi = {10.1016/j.neulet.2023.137133}, pmid = {36801241}, issn = {1872-7972}, abstract = {It has been confirmed that motor imagery (MI) and motor execution (ME) share a subset of mechanisms underlying motor cognition. In contrast to the well-studied laterality of upper limb movement, the laterality hypothesis of lower limb movement also exists, but it needs to be characterized by further investigation. This study used electroencephalographic (EEG) recordings of 27 subjects to compare the effects of bilateral lower limb movement in the MI and ME paradigms. Event-related potential (ERP) recorded was decomposed into meaningful and useful representatives of the electrophysiological components, such as N100 and P300. Principal components analysis (PCA) was used to trace the characteristics of ERP components temporally and spatially, respectively. The hypothesis of this study is that the functional opposition of unilateral lower limbs of MI and ME should be reflected in the different alterations of the spatial distribution of lateralized activity. Meanwhile, the significant ERP-PCA components of the EEG signals as identifiable feature sets were applied with support vector machine to identify left and right lower limb movement tasks. The average classification accuracy over all subjects is up to 61.85% for MI and 62.94% for ME. The proportion of subjects with significant results are 51.85% for MI and 59.26% for ME, respectively. Therefore, a potential new classification model for lower limb movement can be applied on brain computer interface (BCI) systems in the future.}, }
@article {pmid36800979, year = {2023}, author = {Hambridge, T and Coffeng, LE and de Vlas, SJ and Richardus, JH}, title = {Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach.}, journal = {Infectious diseases of poverty}, volume = {12}, number = {1}, pages = {12}, pmid = {36800979}, issn = {2049-9957}, abstract = {BACKGROUND: Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type.<br><br>METHODS: Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors.<br><br>RESULTS: For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review.<br><br>CONCLUSIONS: The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.}, }
@article {pmid36800288, year = {2023}, author = {Shu, X and Wei, C and Tu, WY and Zhong, K and Qi, S and Wang, A and Bai, L and Zhang, SX and Luo, B and Xu, ZZ and Zhang, K and Shen, C}, title = {Negative regulation of TREM2-mediated C9orf72 poly-GA clearance by the NLRP3 inflammasome.}, journal = {Cell reports}, volume = {42}, number = {2}, pages = {112133}, doi = {10.1016/j.celrep.2023.112133}, pmid = {36800288}, issn = {2211-1247}, abstract = {Expansion of the hexanucleotide repeat GGGGCC in the C9orf72 gene is the most common genetic factor in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Poly-Gly-Ala (poly-GA), one form of dipeptide repeat proteins (DPRs) produced from GGGGCC repeats, tends to form neurotoxic protein aggregates. The C9orf72 GGGGCC repeats and microglial receptor TREM2 are both associated with risk for ALS/FTD. The role and regulation of TREM2 in C9orf72-ALS/FTD remain unclear. Here, we found that poly-GA proteins activate the microglial NLRP3 inflammasome to produce interleukin-1β (IL-1β), which promotes ADAM10-mediated TREM2 cleavage and inhibits phagocytosis of poly-GA. The inhibitor of the NLRP3 inflammasome, MCC950, reduces the TREM2 cleavage and poly-GA aggregates, resulting in the alleviation of motor deficits in poly-GA mice. Our study identifies a crosstalk between NLRP3 and TREM2 signaling, suggesting that targeting the NLRP3 inflammasome to sustain TREM2 is an approach to treat C9orf72-ALS/FTD.}, }
@article {pmid36799296, year = {2023}, author = {Liu, Y and Shen, X and Zhang, Y and Zheng, X and Cepeda, C and Wang, Y and Duan, S and Tong, X}, title = {Interactions of glial cells with neuronal synapses, from astrocytes to microglia and oligodendrocyte lineage cells.}, journal = {Glia}, volume = {}, number = {}, pages = {}, doi = {10.1002/glia.24343}, pmid = {36799296}, issn = {1098-1136}, abstract = {The mammalian brain is a complex organ comprising neurons, glia, and more than 1 × 10[14] synapses. Neurons are a heterogeneous group of electrically active cells, which form the framework of the complex circuitry of the brain. However, glial cells, which are primarily divided into astrocytes, microglia, oligodendrocytes (OLs), and oligodendrocyte precursor cells (OPCs), constitute approximately half of all neural cells in the mammalian central nervous system (CNS) and mainly provide nutrition and tropic support to neurons in the brain. In the last two decades, the concept of "tripartite synapses" has drawn great attention, which emphasizes that astrocytes are an integral part of the synapse and regulate neuronal activity in a feedback manner after receiving neuronal signals. Since then, synaptic modulation by glial cells has been extensively studied and substantially revised. In this review, we summarize the latest significant findings on how glial cells, in particular, microglia and OL lineage cells, impact and remodel the structure and function of synapses in the brain. Our review highlights the cellular and molecular aspects of neuron-glia crosstalk and provides additional information on how aberrant synaptic communication between neurons and glia may contribute to neural pathologies.}, }
@article {pmid36799225, year = {2023}, author = {Tu, WY and Xu, W and Zhang, J and Qi, S and Bai, L and Shen, C and Zhang, K}, title = {C9orf72 poly-GA proteins impair neuromuscular transmission.}, journal = {Zoological research}, volume = {44}, number = {2}, pages = {331-340}, doi = {10.24272/j.issn.2095-8137.2022.356}, pmid = {36799225}, issn = {2095-8137}, abstract = {Amyotrophic lateral sclerosis (ALS) is a devastating motoneuron disease, in which lower motoneurons lose control of skeletal muscles. Degeneration of neuromuscular junctions (NMJs) occurs at the initial stage of ALS. Dipeptide repeat proteins (DPRs) from G4C2 repeat-associated non-ATG (RAN) translation are known to cause C9orf72-associated ALS (C9-ALS). However, DPR inclusion burdens are weakly correlated with neurodegenerative areas in C9-ALS patients, indicating that DPRs may exert cell non-autonomous effects, in addition to the known intracellular pathological mechanisms. Here, we report that poly-GA, the most abundant form of DPR in C9-ALS, is released from cells. Local administration of poly-GA proteins in peripheral synaptic regions causes muscle weakness and impaired neuromuscular transmission in vivo. The NMJ structure cannot be maintained, as evidenced by the fragmentation of postsynaptic acetylcholine receptor (AChR) clusters and distortion of presynaptic nerve terminals. Mechanistic study demonstrated that extracellular poly-GA sequesters soluble Agrin ligands and inhibits Agrin-MuSK signaling. Our findings provide a novel cell non-autonomous mechanism by which poly-GA impairs NMJs in C9-ALS. Thus, targeting NMJs could be an early therapeutic intervention for C9-ALS.}, }
@article {pmid36798295, year = {2023}, author = {Pollmann, EH and Yin, H and Uguz, I and Dubey, A and Wingel, KE and Choi, JS and Moazeni, S and Gilhotra, Y and Pavlovsky, VA and Banees, A and Boominathan, V and Robinson, J and Veeraraghavan, A and Pieribone, VA and Pesaran, B and Shepard, KL}, title = {Subdural CMOS optical probe (SCOPe) for bidirectional neural interfacing.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.07.527500}, pmid = {36798295}, abstract = {Optical neurotechnologies use light to interface with neurons and can monitor and manipulate neural activity with high spatial-temporal precision over large cortical extents. While there has been significant progress in miniaturizing microscope for head-mounted configurations, these existing devices are still very bulky and could never be fully implanted. Any viable translation of these technologies to human use will require a much more noninvasive, fully implantable form factor. Here, we leverage advances in microelectronics and heterogeneous optoelectronic packaging to develop a transformative, ultrathin, miniaturized device for bidirectional optical stimulation and recording: the subdural CMOS Optical Probe (SCOPe). By being thin enough to lie entirely within the subdural space of the primate brain, SCOPe defines a path for the eventual human translation of a new generation of brain-machine interfaces based on light.}, }
@article {pmid36795966, year = {2023}, author = {Ghoreifi, A and Seyedian, SL and Piatti, P and Chew, YC and Jara, B and Sanossian, L and Bhasin, JM and Yamada, T and Fuchs, G and Bhanvadia, S and Sotelo, R and Hung, A and Aron, M and Desai, M and Gill, I and Daneshmand, S and Liang, G and Djaladat, H}, title = {A Urine-Based DNA Methylation Marker Test to Detect Upper Tract Urothelial Carcinoma: A Prospective Cohort Study.}, journal = {The Journal of urology}, volume = {}, number = {}, pages = {101097JU0000000000003188}, doi = {10.1097/JU.0000000000003188}, pmid = {36795966}, issn = {1527-3792}, abstract = {PURPOSE: To explore the accuracy of a urine-based epigenetic test for detecting upper tract urothelial carcinoma (UTUC).<br><br>MATERIALS AND METHODS: Under an IRB-approved protocol, urine samples were prospectively collected from primary UTUC patients before radical nephroureterectomy (RNU), ureterectomy, or ureteroscopy (URS) between December 2019 and March 2022. Samples were analyzed with Bladder CARE, a urine-based test that measures the methylation levels of 3 cancer biomarkers (TRNA-Cys, SIM2, and NKX1-1) and 2 internal control loci using methylation-sensitive restriction enzymes coupled with qPCR. Results were reported as the Bladder CARE Index (BCI) score and quantitatively categorized as positive (BCI >5), high-risk (BCI 2.5-5), or negative (BCI <2.5). The findings were compared with those of 1:1 sex/age-matched cancer-free healthy individuals.<br><br>RESULTS: Fifty patients (40 RNU, 7 ureterectomy, and 3 URS) with a median (IQR) age of 72 (64-79) years were included. BCI results were positive in 47, high-risk in one, and negative in 2 patients. A significant correlation was found between BCI values and tumor size. Urine cytology was available for 35 patients, of whom 22 (63%) were false-negative. UTUC patients had significantly higher BCI values compared to the controls (mean BCI 189.3 vs 1.6, respectively; P <.001). The sensitivity, specificity, positive predictive value, and negative predictive value of the Bladder CARE test for detecting UTUC were 96%, 88%, 89%, and 96%, respectively.Conclusions:Bladder CARE is an accurate urine-based epigenetic test for the diagnosis of UTUC, with much higher sensitivity than standard urine cytology.}, }
@article {pmid36792239, year = {2023}, author = {Ziafati, A and Maleki, A}, title = {Genetic algorithm based ensemble system using MLR and MsetCCA methods for SSVEP frequency recognition.}, journal = {Medical engineering &amp; physics}, volume = {111}, number = {}, pages = {103945}, doi = {10.1016/j.medengphy.2022.103945}, pmid = {36792239}, issn = {1873-4030}, abstract = {BCI systems provide a direct communication channel between the human and the machine using brain signals. Among the various methods of steady-state visual evoked potential (SSVEP) stimulation frequency detection, multiple linear regression (MLR), and multiset canonical correlation analysis (MsetCCA) methods have achieved high accurate results in recent studies. The purpose of this study is to utilize both approaches and benefit from them using a genetic algorithm (GA). This algorithm leads to high-performance optimization due to its large number of regulatory parameters. Signal analysis was performed for the windows with 0.5 to 4 s duration length and with 0.5-second incremental steps. In this paper, we were able to achieve 100% accuracy of recognition for 2-second time-windows using the genetic algorithm to optimally ensemble SSVEP stimulation frequency detection methods. The accuracy of the proposed system indicates a significant improvement in detection compared to either MLR or MsetCCA alone and indicates that the ensemble system is correctly optimized using the genetic algorithm. Genetic algorithm is one of the most widely used algorithms because of its high regulatory parameters leading to its high flexibility. The improvement in detection of the proposed system is due to the use of the strengths of both two methods, and the optimal choice of the system response to visual stimuli.}, }
@article {pmid36789010, year = {2023}, author = {Matsuki, E and Kawamoto, S and Morikawa, Y and Yahagi, N}, title = {The Impact of Cold Ambient Temperature in the Pattern of Influenza Virus Infection.}, journal = {Open forum infectious diseases}, volume = {10}, number = {2}, pages = {ofad039}, pmid = {36789010}, issn = {2328-8957}, abstract = {BACKGROUND: Prior literature suggests that cold temperature strongly influences the immune function of animals and human behaviors, which may allow for the transmission of respiratory viral infections. However, information on the impact of cold stimuli, especially the impact of temporal change in the ambient temperature on influenza virus transmission, is limited.<br><br>METHODS: A susceptible-infected-recovered-susceptible model was applied to evaluate the effect of temperature change on influenza virus transmission.<br><br>RESULTS: The mean temperature of the prior week was positively associated with the number of newly diagnosed cases (0.107 [95% Bayesian credible interval {BCI}, .106-.109]), whereas the mean difference in the temperature of the prior week was negatively associated (-0.835 [95% BCI, -.840 to -.830]). The product of the mean temperature and mean difference in the temperature of the previous week were also negatively associated with the number of newly diagnosed cases (-0.192 [95% BCI, -.197 to -.187]).<br><br>CONCLUSIONS: The mean temperature and the mean difference in temperature affected the number of newly diagnosed influenza cases differently. Our data suggest that high ambient temperature and a drop in the temperature and their interaction increase the risk of infection. Therefore, the highest risk of infection is attributable to a steep fall in temperature in a relatively warm environment.}, }
@article {pmid36788214, year = {2023}, author = {Yu, B and Zhang, Q and Lin, L and Zhou, X and Ma, W and Wen, S and Li, C and Wang, W and Wu, Q and Wang, X and Li, XM}, title = {Molecular and cellular evolution of the amygdala across species analyzed by single-nucleus transcriptome profiling.}, journal = {Cell discovery}, volume = {9}, number = {1}, pages = {19}, pmid = {36788214}, issn = {2056-5968}, abstract = {The amygdala, or an amygdala-like structure, is found in the brains of all vertebrates and plays a critical role in survival and reproduction. However, the cellular architecture of the amygdala and how it has evolved remain elusive. Here, we generated single-nucleus RNA-sequencing data for more than 200,000 cells in the amygdala of humans, macaques, mice, and chickens. Abundant neuronal cell types from different amygdala subnuclei were identified in all datasets. Cross-species analysis revealed that inhibitory neurons and inhibitory neuron-enriched subnuclei of the amygdala were well-conserved in cellular composition and marker gene expression, whereas excitatory neuron-enriched subnuclei were relatively divergent. Furthermore, LAMP5[+] interneurons were much more abundant in primates, while DRD2[+] inhibitory neurons and LAMP5[+]SATB2[+] excitatory neurons were dominant in the human central amygdalar nucleus (CEA) and basolateral amygdalar complex (BLA), respectively. We also identified CEA-like neurons and their species-specific distribution patterns in chickens. This study highlights the extreme cell-type diversity in the amygdala and reveals the conservation and divergence of cell types and gene expression patterns across species that may contribute to species-specific adaptations.}, }
@article {pmid36787644, year = {2023}, author = {Nardelli, M and Citi, L and Barbieri, R and Valenza, G}, title = {Characterization of autonomic states by complex sympathetic and parasympathetic dynamics.}, journal = {Physiological measurement}, volume = {}, number = {}, pages = {}, doi = {10.1088/1361-6579/acbc07}, pmid = {36787644}, issn = {1361-6579}, abstract = {Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises specially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.}, }
@article {pmid36786985, year = {2022}, author = {Bobrova, EV and Reshetnikova, VV and Vershinina, EA and Grishin, AA and Isaev, MR and Bobrov, PD and Gerasimenko, YP}, title = {Dependence of Brain-Computer Interface Control Training on Personality Traits.}, journal = {Doklady. Biochemistry and biophysics}, volume = {507}, number = {1}, pages = {273-277}, pmid = {36786985}, issn = {1608-3091}, abstract = {Personality traits (PTs) are predictors of the success of control of brain-computer interfaces (BCIs); however, it is unknown how the PTs that are optimal for BCI control changes during training. The paper for the first time analyzes the correlations between PTs and the accuracy of the classification (AC) of brain states in imagining the movements of the hands, feet, and locomotion during 10-day training of ten volunteers in BCI control. In the first 3 days of training, the AC is higher for more stressed and anxious volunteers; in the last days, for calmer ones. In the middle of the training period, AC is higher in low-demonstrativeness persons, it is more pronounced when imagining foot movements. Correlations of low demonstrativeness, as well as of foresight and self-control with AC when imagining foot movements are revealed significantly more often than when imagining hand movements and locomotions. During almost the entire period of training, AC with locomotion imagination is higher in individualists. The results make it possible to propose individually-oriented recommendations for the use of BCI based on the imagination of movements for the rehabilitation of patients with motor disorders.}, }
@article {pmid36780814, year = {2023}, author = {Mattheiss, JP and Breyta, R and Kurath, G and LaDeau, SL and Páez, DJ and Ferguson, PFB}, title = {Coproduction and modeling spatial contact networks prevent bias about infectious hematopoietic necrosis virus transmission for Snake River Basin salmonids.}, journal = {Journal of environmental management}, volume = {334}, number = {}, pages = {117415}, doi = {10.1016/j.jenvman.2023.117415}, pmid = {36780814}, issn = {1095-8630}, abstract = {Much remains unknown about variation in pathogen transmission across the geographic range of a free-ranging fish or animal species and about the influence of movement (associated with husbandry practices or animal behavior) on pathogen transmission. Salmonid hatcheries are an ideal system in which to study these processes. Salmonid hatcheries are managed for endangered species recovery, supplementation of threatened or at-risk fish stocks, support of fisheries, and ecosystem stability. Infectious hematopoietic necrosis virus (IHNV) is a rhabdovirus of significant concern to salmon aquaculture. Landscape IHNV transmission dynamics previously had been estimated only for salmonid hatcheries in the Lower Columbia River Basin (LCRB). The objectives of this study were to estimate IHNV transmission dynamics in a unique geographic region, the Snake River Basin (SRB), and to quantitatively estimate the effect of model coproduction on inference because previous assessments of coproduction have been qualitative. In contrast to the LCRB, the SRB has hatchery complexes consisting of a main hatchery and ≥1 satellite facility. Knowledge about hatchery complexes was held by a subset of project researchers but would not have been available to project modelers without coproduction. Project modelers generated and tested multiple versions of Bayesian susceptible-exposedinfected models to realistically represent the SRB and estimate the effect of coproduction. Models estimated the frequency of transmission routes, route-specific infection probabilities, and infection probabilities for combinations of salmonid hosts and IHNV lineages. Model results indicated that in the SRB, avoiding exposure to IHNV-positive adult salmonids is the most important action to prevent juvenile infections. Migrating adult salmonids exposed juvenile cohort-sites most frequently, and the infection probability was greatest following exposure to migrating adults. Without coproduction, the frequency of exposure by migrating adults would have been overestimated by 70 cohort-sites, and the infection probability following exposure to migrating adults would have been underestimated by∼0.09. The coproduced model had less uncertainty in the infection probability if no transmission route could be identified (Bayesian credible interval (BCI) width = 0.12) compared to the model without coproduction (BCI width = 0.34). Evidence for virus lineage MD specialization on steelhead and rainbow trout (both Oncorhynchus mykiss) was apparent without model coproduction. In the SRB, we found a greater probability of virus lineage UC infection in Chinook salmon (Oncorhynchus tshawytscha) compared to in O. mykiss, whereas in the LCRB, UC more clearly exhibited a generalist approach. Coproduction influenced estimates that depended on transmission routes, which operated differently at main hatcheries and satellite sites within hatchery complexes. Hatchery complexes are found outside of the SRB and are not specific to salmonid hatcheries alone. There is great potential for coproduction and modeling spatial contact networks to advance understanding about infectious disease transmission in complex production systems and surrounding free-ranging animal populations.}, }
@article {pmid36778458, year = {2023}, author = {Wilson, GH and Willett, FR and Stein, EA and Kamdar, F and Avansino, DT and Hochberg, LR and Shenoy, KV and Druckmann, S and Henderson, JM}, title = {Long-term unsupervised recalibration of cursor BCIs.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.03.527022}, pmid = {36778458}, abstract = {Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.}, }
@article {pmid36778360, year = {2023}, author = {Chen, K and Cambi, F and Kozai, TDY}, title = {Pro-myelinating Clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.01.31.526463}, pmid = {36778360}, abstract = {Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over chronic implantation. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.}, }
@article {pmid36776946, year = {2022}, author = {Chen, XJ and Collins, LM and Mainsah, BO}, title = {Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.}, journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics}, volume = {2022}, number = {}, pages = {1642-1647}, pmid = {36776946}, issn = {1062-922X}, abstract = {Brain-computer interfaces (BCIs), such as the P300 speller, can provide a means of communication for individuals with severe neuromuscular limitations. BCIs interpret electroencephalography (EEG) signals in order to translate embedded information about a user's intent into executable commands to control external devices. However, EEG signals are inherently noisy and nonstationary, posing a challenge to extended BCI use. Conventionally, a BCI classifier is trained via supervised learning in an offline calibration session; once trained, the classifier is deployed for online use and is not updated. As the statistics of a user's EEG data change over time, the performance of a static classifier may decline with extended use. It is therefore desirable to automatically adapt the classifier to current data statistics without requiring offline recalibration. In an existing semi-supervised learning approach, the classifier is trained on labeled EEG data and is then updated using incoming unlabeled EEG data and classifier-predicted labels. To reduce the risk of learning from incorrect predictions, a threshold is imposed to exclude unlabeled data with low-confidence label predictions from the expanded training set when retraining the adaptive classifier. In this work, we propose the use of a language model for spelling error correction and disambiguation to provide information about label correctness during semi-supervised learning. Results from simulations with multi-session P300 speller user EEG data demonstrate that our language-guided semi-supervised approach significantly improves spelling accuracy relative to conventional BCI calibration and threshold-based semi-supervised learning.}, }
@article {pmid36776560, year = {2023}, author = {Li, S and Al-Sheikh, U and Chen, Y and Kang, L}, title = {Nematode homologs of the sour taste receptor Otopetrin1 are evolutionarily conserved acid-sensitive proton channels.}, journal = {Frontiers in cell and developmental biology}, volume = {11}, number = {}, pages = {1133890}, pmid = {36776560}, issn = {2296-634X}, abstract = {Numerous taste receptors and related molecules have been identified in vertebrates and invertebrates. Otopetrin1 has recently been identified as mammalian sour taste receptor which is essential for acid sensation. However, whether other Otopetrin proteins are involved in PH-sensing remains unknown. In C. elegans, there are eight otopetrin homologous genes but their expression patterns and functions have not been reported so far. Through heterologous expression in HEK293T cells, we found that ceOTOP1a can be activated by acid in NMDG[+] solution without conventional cations, which generated inward currents and can be blocked by zinc ions. Moreover, we found that Otopetrin channels are widely expressed in numerous tissues, especially in sensory neurons in the nematode. These results suggest that the biophysical characteristics of the Otopetrin channels in nematodes are generally conserved. However, a series of single gene mutations of otopetrins, which were constructed by CRISPR-Cas9 method, did not affect either calcium responses in ASH polymodal sensory neurons to acid stimulation or acid avoidance behaviors, suggesting that Otopetrin channels might have diverse functions among species. This study reveals that nematode Otopetrins are evolutionarily conserved acid-sensitive proton channels, and provides a framework for further revealing the function and mechanisms of Otopetrin channels in both invertebrates and vertebrates.}, }
@article {pmid36776220, year = {2022}, author = {Cajigas, I and Davis, KC and Prins, NW and Gallo, S and Naeem, JA and Fisher, L and Ivan, ME and Prasad, A and Jagid, JR}, title = {Brain-Computer interface control of stepping from invasive electrocorticography upper-limb motor imagery in a patient with quadriplegia.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1077416}, pmid = {36776220}, issn = {1662-5161}, abstract = {Introduction: Most spinal cord injuries (SCI) result in lower extremities paralysis, thus diminishing ambulation. Using brain-computer interfaces (BCI), patients may regain leg control using neural signals that actuate assistive devices. Here, we present a case of a subject with cervical SCI with an implanted electrocorticography (ECoG) device and determined whether the system is capable of motor-imagery-initiated walking in an assistive ambulator. Methods: A 24-year-old male subject with cervical SCI (C5 ASIA A) was implanted before the study with an ECoG sensing device over the sensorimotor hand region of the brain. The subject used motor-imagery (MI) to train decoders to classify sensorimotor rhythms. Fifteen sessions of closed-loop trials followed in which the subject ambulated for one hour on a robotic-assisted weight-supported treadmill one to three times per week. We evaluated the stability of the best-performing decoder over time to initiate walking on the treadmill by decoding upper-limb (UL) MI. Results: An online bagged trees classifier performed best with an accuracy of 84.15% averaged across 9 weeks. Decoder accuracy remained stable following throughout closed-loop data collection. Discussion: These results demonstrate that decoding UL MI is a feasible control signal for use in lower-limb motor control. Invasive BCI systems designed for upper-extremity motor control can be extended for controlling systems beyond upper extremity control alone. Importantly, the decoders used were able to use the invasive signal over several weeks to accurately classify MI from the invasive signal. More work is needed to determine the long-term consequence between UL MI and the resulting lower-limb control.}, }
@article {pmid36772822, year = {2023}, author = {Arioka, M and Koyano, K and Nakao, Y and Ozaki, M and Nakamura, S and Kiuchi, H and Okada, H and Itoh, S and Murao, K and Kusaka, T}, title = {Quantitative effects of bilirubin structural photoisomers on the measurement of direct bilirubin via the vanadate oxidation method.}, journal = {Annals of clinical biochemistry}, volume = {}, number = {}, pages = {45632231154748}, doi = {10.1177/00045632231154748}, pmid = {36772822}, issn = {1758-1001}, abstract = {BACKGROUND: Exposing blood serum samples to ambient white light-emitting diode (WLED) light may accelerate bilirubin photoisomer production. We previously demonstrated the quantitative effect of bilirubin configurational isomers (BCI) on direct bilirubin (DB) value using the vanadate oxidation method. However, the effects of bilirubin structural photoisomers (BSI) remain unclear.<br><br>METHODS: In Study 1, the relationship between WLED irradiation time and BSI production was examined. Serum samples from five neonates were irradiated with WLED light for 0, 10, 30, 60 and 180 min. Bilirubin isomer concentration and BSI production rates were calculated. In Study 2, we performed quantitative investigation of BSI effect on DB values: Differences in DB, BCI and BSI values before and after irradiation were calculated as &#x22bf;DB, &#x22bf;BCI and &#x22bf;BSI, respectively. Assuming the coefficient of BCI affecting DB values was 'a', relational expression was &#x22bf;DB = a*&#x22bf;BSI + 0.19*&#x22bf;BCI. Serum samples from 15 neonates were irradiated with green LED light for 10 and 30&#xa0;s. The respective bilirubin isomer levels were measured, and the coefficient was derived.<br><br>RESULTS: In Study 1, the median BSI production rate was 0.022 mg/dL per min in specimens with an unconjugated bilirubin concentration of 10.88 mg/dL. In Study 2, assuming that &#x22bf;DB-0.19*&#x22bf;BCI was Y and &#x22bf;BSI was X, the relational expression was Y = 0.34X-0.03 (R[2] = 0.87; p < .01) and a = 0.34.<br><br>CONCLUSIONS: Under ambient WLED light, serum sample generated 1.3 mg/dL BSIs in 1 h. Approximately 34% (0.44&#xa0;mg/dL) of BSI concentrations was measured as DB when using the vanadate oxidation method according to the above equation.}, }
@article {pmid36772744, year = {2023}, author = {Yang, L and Van Hulle, MM}, title = {Real-Time Navigation in Google Street View[[®]] Using a Motor Imagery-Based BCI.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031704}, pmid = {36772744}, issn = {1424-8220}, abstract = {Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair-the latter raises safety concerns-thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View[[®]]. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes' lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.}, }
@article {pmid36772731, year = {2023}, author = {Ranieri, A and Pichiorri, F and Colamarino, E and de Seta, V and Mattia, D and Toppi, J}, title = {Parallel Factorization to Implement Group Analysis in Brain Networks Estimation.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031693}, pmid = {36772731}, issn = {1424-8220}, abstract = {When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and real datasets. The PARAFAC approach was solved using three different numbers of rank-one tensors (PAR-FACT). Synthetic data were parametrized according to different levels of three parameters: network dimension (NODES), number of observations (SAMPLE-SIZE), and noise (SWAP-CON) in order to investigate the way they affect the grand average estimation. PARAFAC was then tested on a real connectivity dataset, derived from EEG data of 17 healthy subjects performing wrist extension with left and right hand separately. Findings on both synthetic and real data revealed the potential of the PARAFAC algorithm as a useful tool for grand average extraction. As expected, the best performances in terms of FPR, FNR, and AUC were achieved for great values of sample size and low noise level. A crucial role has been revealed for the PAR-FACT parameter, revealing that an increase in the number of rank-one tensors solving the PARAFAC problem leads to an increase in FPR values and, thus, to a worse grand average estimation.}, }
@article {pmid36772444, year = {2023}, author = {Li, M and Qiu, M and Kong, W and Zhu, L and Ding, Y}, title = {Fusion Graph Representation of EEG for Emotion Recognition.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031404}, pmid = {36772444}, issn = {1424-8220}, abstract = {Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is that they only consider a single relationship of EEG electrodes, which results an incomprehensive representation of EEG data and relatively low accuracy of emotion recognition. In this paper, we propose a fusion graph convolutional network (FGCN) to extract various relations existing in EEG data and fuse these extracted relations to represent EEG data more comprehensively for emotion recognition. First, the FGCN mines brain connection features on topology, causality, and function. Then, we propose a local fusion strategy to fuse these three graphs to fully utilize the valuable channels with strong topological, causal, and functional relations. Finally, the graph convolutional neural network is adopted to represent EEG data for emotion recognition better. Experiments on SEED and SEED-IV demonstrate that fusing different relation graphs are effective for improving the ability in emotion recognition. Furthermore, the emotion recognition accuracy of 3-class and 4-class is higher than that of other state-of-the-art methods.}, }
@article {pmid36772343, year = {2023}, author = {Ron-Angevin, R and Fernández-Rodríguez, &#xc1; and Dupont, C and Maigrot, J and Meunier, J and Tavard, H and Lespinet-Najib, V and André, JM}, title = {Comparison of Two Paradigms Based on Stimulation with Images in a Spelling Brain-Computer Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031304}, pmid = {36772343}, issn = {1424-8220}, abstract = {A P300-based speller can be used to control a home automation system via brain activity. Evaluation of the visual stimuli used in a P300-based speller is a common topic in the field of brain-computer interfaces (BCIs). The aim of the present work is to compare, using the usability approach, two types of stimuli that have provided high performance in previous studies. Twelve participants controlled a BCI under two conditions, which varied in terms of the type of stimulus employed: a red famous face surrounded by a white rectangle (RFW) and a range of neutral pictures (NPs). The usability approach included variables related to effectiveness (accuracy and information transfer rate), efficiency (stress and fatigue), and satisfaction (pleasantness and System Usability Scale and Affect Grid questionnaires). The results indicated that there were no significant differences in effectiveness, but the system that used NPs was reported as significantly more pleasant. Hence, since satisfaction variables should also be considered in systems that potential users are likely to employ regularly, the use of different NPs may be a more suitable option than the use of a single RFW for the development of a home automation system based on a visual P300-based speller.}, }
@article {pmid36772275, year = {2023}, author = {Yedukondalu, J and Sharma, LD}, title = {Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031235}, pmid = {36772275}, issn = {1424-8220}, abstract = {Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.}, }
@article {pmid36772115, year = {2023}, author = {Lin, CF and Lin, HC}, title = {IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031078}, pmid = {36772115}, issn = {1424-8220}, abstract = {This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert-Huang transformation (HHT) time frequency (TF) analysis method. Accordingly, F5 and F6 EEG signal TF energy feature distributions in delta (0.5-4 Hz) rhythm are explored. We propose IMF-based and residue function (RF)-based MF and HS feature information extraction methods with IMFRFERDD (IMFRF energy refereed distribution density), IMFRFMFERDD (IMFRF MF energy refereed distribution density), and IMFRFHSERDD (IMFRF HS energy refereed distribution density) parameters using HHT with application to AM, MI EEG F5, and F6 signals in delta rhythm. The AM and MI tasks involve simultaneously opening fists and feet, as well as simultaneously closing fists and feet. Eight samples (32 in total) with a time duration of 1000 ms are extracted for analyzing F5AM, F5MI, F6AM, and F6MI EEG signals, which are decomposed into five IMFs and one RF. The maximum average IMFRFERDD values of IMF4 are 3.70, 3.43, 3.65, and 3.69 for F5AM, F5MI, F6 AM, and F6MI, respectively. The maximum average IMFRFMFERDD values of IMF4 in the delta rhythm are 21.50, 20.15, 21.02, and 17.30, for F5AM, F5MI, F6AM, and F6MI, respectively. Additionally, the maximum average IMFRFHSERDD values of IMF4 in delta rhythm are 39,21, 39.14, 36.29, and 33.06 with time intervals of 500-600, 800-900, 800-900, and 500-600 ms, for F5AM, F5MI, F6AM, and F6MI, respectively. The results of this study, advance our understanding of meaningful feature information of F5MM, F5MI, F6MM, and F6MI, enabling the design of MI-based brain-computer interface assistive devices for disabled persons.}, }
@article {pmid36767088, year = {2023}, author = {Alvarado, C and Castillo-Aguilar, M and Villegas, V and Estrada Goic, C and Harris, K and Barria, P and Moraes, MM and Mendes, TT and Arantes, RME and Valdés-Badilla, P and Núñez-Espinosa, C}, title = {Physical Activity, Seasonal Sensitivity and Psychological Well-Being of People of Different Age Groups Living in Extreme Environments.}, journal = {International journal of environmental research and public health}, volume = {20}, number = {3}, pages = {}, doi = {10.3390/ijerph20031719}, pmid = {36767088}, issn = {1660-4601}, abstract = {Physical activity can prevent many organic and mental pathologies. For people living in extreme southern high-latitude environments, weather conditions can affect these activities, altering their psychological well-being and favoring the prevalence of seasonal sensitivity (SS). This study aims to determine the relationships between the practice of physical activity, seasonal sensitivity and well-being in people living in high southern latitudes. A cross-sectional study was conducted, using the Seasonal Pattern Assessment Questionnaire (SPAQ), applying a psychological well-being scale, and determining sports practice according to the recommendations of the World Health Organization (WHO) for the 370 male (n = 209; 55%) and female (n = 173; 45%) participants. The main results indicated that 194 people (52 ± 7.7 years) reported physical activity. High-intensity physical activity practitioners recorded a significantly lower proportion of SS. In terms of psychological well-being, an adverse effect was found between the Seasonal Score Index (SSI) and five subcategories of the Ryff well-being scale. In conclusion, those who perform high-intensity physical activity have a lower SS, and those who have a higher SS have a lower psychological well-being.}, }
@article {pmid36766680, year = {2023}, author = {Küçükakarsu, M and Kavsaoğlu, AR and Alenezi, F and Alhudhaif, A and Alwadie, R and Polat, K}, title = {A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain's Electrical Activity Signals.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {13}, number = {3}, pages = {}, doi = {10.3390/diagnostics13030575}, pmid = {36766680}, issn = {2075-4418}, abstract = {This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.}, }
@article {pmid36765392, year = {2023}, author = {Nair, L and Winkle, B and Senanayake, E}, title = {Managing blunt cardiac injury.}, journal = {Journal of cardiothoracic surgery}, volume = {18}, number = {1}, pages = {71}, pmid = {36765392}, issn = {1749-8090}, abstract = {Blunt cardiac injury (BCI) encompasses a spectrum of pathologies ranging from clinically silent, transient arrhythmias to deadly cardiac wall rupture. Of diagnosed BCIs, cardiac contusion is most common. Suggestive symptoms may be unrelated to BCI, while some injuries may be clinically asymptomatic. Cardiac rupture is the most devastating complication of BCI. Most patients who sustain rupture of a heart chamber do not reach the emergency department alive. The incidence of BCI following blunt thoracic trauma remains variable and no gold standard exists to either diagnose cardiac injury or provide management. Diagnostic tests should be limited to identifying those patients who are at risk of developing cardiac complications as a result of cardiac in jury. Therapeutic interventions should be directed to treat the complications of cardiac injury. Prompt, appropriate and well-orchestrated surgical treatment is invaluable in the management of the unstable patients.}, }
@article {pmid36765285, year = {2023}, author = {Bae, CM and Cho, JY and Jung, H and Son, SA}, title = {Serum pro-B-type natriuretic peptide levels and cardiac index as adjunctive tools of blunt cardiac injury.}, journal = {BMC cardiovascular disorders}, volume = {23}, number = {1}, pages = {81}, pmid = {36765285}, issn = {1471-2261}, abstract = {BACKGROUND: Blunt cardiac injury (BCI) has a variety of symptoms that may be a potentially life-threatening injury that can lead to death. Depending on the diagnosis of BCI, treatment direction and length of stay may vary. In addition, the utility of other diagnostic tests for cardiac disease as diagnostic tools for BCI remain unclear. The purpose of this study was to investigate the competence of N-terminal pro-B-type natriuretic peptide (NT pro-BNP) and cardiac index (C.I) as adjunctive diagnostic tools for BCI.<br><br>METHODS: From January 2018 to March 2020, severe trauma patients with sternum fracture who were admitted to the traumatic intensive care unit (TICU) were included this study. Patients with sternum fracture, 18&#xa0;years of age or older, and with an injury severity score&#x2009;>&#x2009;16 who required intensive care were included. Invasive measurement for the analysis of the pulse contour for C.I monitoring and intravenous blood sampling for NT pro-BNP measurement were performed. Sampling and 12-lead electrocardiogram were performed at different time points as follows: immediately after TICU admission and at 24&#xa0;h and 48&#xa0;h after trauma.<br><br>RESULTS: Among 103; 33 patients with factors that could affect NT pro-BNP were excluded; therefore, 63 patients were included in this study. According to the American Association for the Surgery of Trauma Cardiac Injury Scale, 33 patients were diagnosed with non-BCI, and 30 patients constituted with BCI. The median ages of the patients were 58 (52-69), and 60 (45-69) years in the non-BCI and BCI groups, respectively (p&#x2009;=&#x2009;0.77). The median NT pro-BNP values were higher in the BCI group on admission, hospital day (HD) 2, and HD 3, however, no statistical difference was observed (125 (49-245) vs. 130 (47-428) pg/mL, p&#x2009;=&#x2009;0.08, 124 (68-224) vs. 187 (55-519) pg/mL, p&#x2009;=&#x2009;0.09, and 121(59-225) vs. 133 (56-600) pg/mL, p&#x2009;=&#x2009;0.17, respectively). On the contrary, significantly lower values were observed in the median C.I measurement on admission and HD 3 in the BCI group (3.2 (2.8-3.5) vs. 2.6 (2.3-3.5) L/min/m[2], p&#x2009;<&#x2009;0.01 and 3.2 (3.1-3.9) vs. 2.9 (2.4-3.2) L/min/m[2], p&#x2009;<&#x2009;0.01, respectively); however, no significant difference was observed on HD 2 (3.4 (3.0-3.7) vs. 2.6 (2.4-3.4) L/min/m[2], p&#x2009;=&#x2009;0.17), Furthermore, The median lactate levels in the BCI group upon admission, HD 2, and HD 3 were significantly higher than those in the non-BCI group (1.8 (1.1-2.6) vs. 3.1 (2.1-4.4) mmol/L, p&#x2009;<&#x2009;0.01; 1.3 (0.8-2.3) vs. 3.0 (2.2-4.7) mmol/L, p&#x2009;<&#x2009;0.01; and 1.5 (0.9-1.5) vs. 2.2 (1.3-3.7) mmol/L, p&#x2009;<&#x2009;0.01, respectively).<br><br>CONCLUSION: Consecutive values of NT pro-BNP and C.I show no correlation with ECG-based BCI diagnosis. However, lactate level measurement may help in the early recognition of BCI as an adjunctive tool. It should be noted that this is a hypothesis-generating study for BCI diagnosis. Further studies should be conducted in larger populations with a prospective approach.}, }
@article {pmid36765121, year = {2023}, author = {Hinss, MF and Jahanpour, ES and Somon, B and Pluchon, L and Dehais, F and Roy, RN}, title = {Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.}, journal = {Scientific data}, volume = {10}, number = {1}, pages = {85}, pmid = {36765121}, issn = {2052-4463}, abstract = {Brain-Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. Testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce. The COG-BCI database encompasses the recordings of 29 participants over 3 separate sessions with 4 different tasks (MATB, N-Back, PVT, Flanker) designed to elicit different mental states, for a total of over 100 hours of open EEG data. This dataset was validated on a subjective, behavioral and physiological level, to ensure its usefulness to the pBCI community. Furthermore, a proof of concept is given with an example of mental workload estimation pipeline and results, to ensure that the data can be used for the design and evaluation of pBCI pipelines. This body of work presents a large effort to promote the use of pBCIs in an open science framework.}, }
@article {pmid36764125, year = {2023}, author = {Kong, LZ and Shen, YT and Zhang, DH and Lai, JB and Hu, SH}, title = {Free long-acting injectables for patients with psychosis: A step forward.}, journal = {Asian journal of psychiatry}, volume = {83}, number = {}, pages = {103476}, doi = {10.1016/j.ajp.2023.103476}, pmid = {36764125}, issn = {1876-2026}, }
@article {pmid36763992, year = {2023}, author = {Chen, J and Wang, D and Yi, W and Xu, M and Tan, X}, title = {Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbb2c}, pmid = {36763992}, issn = {1741-2552}, abstract = {Objective.Motor Imagery Brain-Computer Interface (MI-BCI) is an active Brain-Computer Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the most important non-invasive BCI paradigms. In MI-BCI studies, deep learning-based methods (especially lightweight networks) have attracted more attention in recent years, but the decoding performance still needs further improving.Approach.To solve this problem, we designed a filter bank structure with sinc-convolutional layers for spatio-temporal feature extraction of MI-EEG in four motor rhythms. The Channel Self-Attention method was introduced for feature selection based on both global and local information, so as to build a model called Filter Bank Sinc-convolutional Network with Channel Self-Attention (FB-Sinc-SCANet) for high performance MI-decoding. Also, we proposed a data augmentation method based on Multivariate Empirical Mode Decomposition (MEMD) to improve the generalization capability of the model.Main results.We performed an intra-subject evaluation experiment on the unseen data of three open MI datasets. The proposed method achieved mean accuracy of 78.20% (4-class scenario) on BCI Competition IV IIa, 87.34% (2-class scenario) on BCI Competition IV IIb, and 72.03% (2-class scenario) on OpenBMI dataset, which are significantly higher than those of compared deep learning-based methods by at least 3.05% (p=0.0469), 3.18% (p=0.0371), and 2.27% (p=0.0024) respectively.Significance.This work provides a new option for deep learning-based MI decoding, which can be employed for building BCI systems for motor rehabilitation.}, }
@article {pmid36763058, year = {2022}, author = {Ma, D and Zhong, L and Yan, Z and Yao, J and Zhang, Y and Ye, F and Huang, Y and Lai, D and Yang, W and Hou, P and Guo, J}, title = {Structural mechanisms for the activation of human cardiac KCNQ1 channel by electro-mechanical coupling enhancers.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {119}, number = {45}, pages = {e2207067119}, doi = {10.1073/pnas.2207067119}, pmid = {36763058}, issn = {1091-6490}, abstract = {The cardiac KCNQ1 potassium channel carries the important IKs current and controls the heart rhythm. Hundreds of mutations in KCNQ1 can cause life-threatening cardiac arrhythmia. Although KCNQ1 structures have been recently resolved, the structural basis for the dynamic electro-mechanical coupling, also known as the voltage sensor domain-pore domain (VSD-PD) coupling, remains largely unknown. In this study, utilizing two VSD-PD coupling enhancers, namely, the membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP2) and a small-molecule ML277, we determined 2.5-3.5 Å resolution cryo-electron microscopy structures of full-length human KCNQ1-calmodulin (CaM) complex in the apo closed, ML277-bound open, and ML277-PIP2-bound open states. ML277 binds at the "elbow" pocket above the S4-S5 linker and directly induces an upward movement of the S4-S5 linker and the opening of the activation gate without affecting the C-terminal domain (CTD) of KCNQ1. PIP2 binds at the cleft between the VSD and the PD and brings a large structural rearrangement of the CTD together with the CaM to activate the PD. These findings not only elucidate the structural basis for the dynamic VSD-PD coupling process during KCNQ1 gating but also pave the way to develop new therapeutics for anti-arrhythmia.}, }
@article {pmid36760796, year = {2022}, author = {Xu, F and Zhao, J and Liu, M and Yu, X and Wang, C and Lou, Y and Shi, W and Liu, Y and Gao, L and Yang, Q and Zhang, B and Lu, S and Tang, J and Leng, J}, title = {Exploration of sleep function connection and classification strategies based on sub-period sleep stages.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1088116}, pmid = {36760796}, issn = {1662-4548}, abstract = {BACKGROUND: As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.<br><br>METHODS: Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.<br><br>RESULTS: The experimental results have shown that when the number of sub-periods is 30, the &#x3b1; (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.<br><br>CONCLUSION: The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.}, }
@article {pmid36760718, year = {2022}, author = {Singh, AK and Krishnan, S}, title = {Trends in EEG signal feature extraction applications.}, journal = {Frontiers in artificial intelligence}, volume = {5}, number = {}, pages = {1072801}, pmid = {36760718}, issn = {2624-8212}, abstract = {This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.}, }
@article {pmid36750362, year = {2023}, author = {Esparza-Iaizzo, M and Vigué-Guix, I and Ruzzoli, M and Torralba, M and Soto-Faraco, S}, title = {Long-range alpha-synchronisation as control signal for BCI: A feasibility study.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0203-22.2023}, pmid = {36750362}, issn = {2373-2822}, abstract = {Shifts in spatial attention are associated with variations in alpha-band (α, 8-14 Hz) activity, specifically in inter-hemispheric imbalance. The underlying mechanism is attributed to local α-synchronisation, which regulates local inhibition of neural excitability, and fronto-parietal synchronisation reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCI). In the present study, we explored whether long-range α-synchronisation presents lateralised patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data shows a lateralised pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronisation with support vector machines (SVM) was at chance-level. The present findings suggest EEG may not be capable of detecting long-range α-synchronisation in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.SIGNIFICANCE STATEMENTCognitive neuroscience advances should ideally have a real-world impact, with an obvious avenue for transference being BCI applications. The hope is to faithfully translate user-generated brain endogenous states into control signals to actuate devices. A paramount challenge for transfer is to move from group-level, multi-trial average approaches to single-trial level. Here, we evaluated the feasibility of single-trial estimation of phase synchrony across distant brain regions. Although many studies link attention to long-range synchrony modulation, this metric has never been used to control BCI. We present a first attempt of a synchrony-based BCI that, albeit unsuccessful, should help break new ground to map endogenous attention shifts to real-time control of brain-computer actuated systems.}, }
@article {pmid36750151, year = {2023}, author = {Kikkert, S and Sonar, HA and Freund, P and Paik, J and Wenderoth, N}, title = {Hand and face somatotopy shown using MRI-safe vibrotactile stimulation with a novel Soft Pneumatic Actuator (SPA)-Skin interface.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119932}, doi = {10.1016/j.neuroimage.2023.119932}, pmid = {36750151}, issn = {1095-9572}, abstract = {The exact somatotopy of the human facial representation in the primary somatosensory cortex (S1) remains debated. One reason that progress has been hampered is due to the methodological challenge of how to apply automated vibrotactile stimuli to face areas in a manner that is: 1) reliable despite differences in the curvatures of face locations; and 2) MR-compatible and free of MR-interference artefacts when applied in the MR head-coil. Here we overcome this challenge by using soft pneumatic actuator (SPA) technology. SPAs are made of a soft silicon material and can be in- or deflated by means of airflow, have a small diameter, and are flexible in structure, enabling good skin contact even on curved body surfaces (as on the face). To validate our approach, we first mapped the well-characterised S1 finger layout using this novel device and confirmed that tactile stimulation of the fingers elicited characteristic somatotopic finger activations in S1. We then used the device to automatically and systematically deliver somatosensory stimulation to different face locations. We found that the forehead representation was least distant from the representation of the hand. Within the face representation, we found that the lip representation is most distant from the forehead representation, with the chin represented in between. Together, our results demonstrate that this novel MR compatible device produces robust and clear somatotopic representational patterns using vibrotactile stimulation through SPA-technology.}, }
@article {pmid36749989, year = {2023}, author = {Massaeli, F and Bagheri, M and Power, SD}, title = {EEG-based detection of modality-specific visual and auditory sensory processing.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb9be}, pmid = {36749989}, issn = {1741-2552}, abstract = {OBJECTIVE: A passive brain-computer interface (pBCI) is a system that enhances a human-machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e., the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the "level" of cognitive resources required (e.g., high vs. low), but we argue that having information regarding the specific "type" of resources (e.g., visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level.<br><br>APPROACH: 15 participants performed carefully designed visual and auditory tasks while EEG data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks.<br><br>MAIN RESULTS: The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance.<br><br>SIGNIFICANCE: These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.}, }
@article {pmid36749645, year = {2023}, author = {Ziemba, AM and Woodson, MCC and Funnell, JL and Wich, D and Balouch, B and Rende, D and Amato, DN and Bao, J and Oprea, I and Cao, D and Bajalo, N and Ereifej, ES and Capadona, JR and Palermo, EF and Gilbert, RJ}, title = {Development of a Slow-Degrading Polymerized Curcumin Coating for Intracortical Microelectrodes.}, journal = {ACS applied bio materials}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsabm.2c00969}, pmid = {36749645}, issn = {2576-6422}, abstract = {Intracortical microelectrodes are used with brain-computer interfaces to restore lost limb function following nervous system injury. While promising, recording ability of intracortical microelectrodes diminishes over time due, in part, to neuroinflammation. As curcumin has demonstrated neuroprotection through anti-inflammatory activity, we fabricated a 300 nm-thick intracortical microelectrode coating consisting of a polyurethane copolymer of curcumin and polyethylene glycol (PEG), denoted as poly(curcumin-PEG1000 carbamate) (PCPC). The uniform PCPC coating reduced silicon wafer hardness by two orders of magnitude and readily absorbed water within minutes, demonstrating that the coating is soft and hydrophilic in nature. Using an in vitro release model, curcumin eluted from the PCPC coating into the supernatant over 1 week; the majority of the coating was intact after an 8-week incubation in buffer, demonstrating potential for longer term curcumin release and softness. Assessing the efficacy of PCPC within a rat intracortical microelectrode model in vivo, there were no significant differences in tissue inflammation, scarring, neuron viability, and myelin damage between the uncoated and PCPC-coated probes. As the first study to implant nonfunctional probes with a polymerized curcumin coating, we have demonstrated the biocompatibility of a PCPC coating and presented a starting point in the design of poly(pro-curcumin) polymers as coating materials for intracortical electrodes.}, }
@article {pmid36745927, year = {2023}, author = {Li, B and Zhang, S and Hu, Y and Lin, Y and Gao, X}, title = {Assembling global and local spatial-temporal filters to extract discriminant information of EEG in RSVP task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb96f}, pmid = {36745927}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interface (BCI) system has developed rapidly in the past decade. And rapid serial visual presentation (RSVP) is an important BCI paradigm to detect the targets in high-speed image streams. For decoding EEG in RSVP task, the ensemble-model methods have better performance than the single-model ones.<br><br>APPROACH: This study proposed a method based on ensemble learning to extract discriminant information of EEG. An extreme gradient boosting (XGB) framework was utilized to sequentially generate the sub models, including one global spatial-temporal filter and a group of local ones. EEG was reconstructed in 3-dimensional form (2-D electrode space &#xd7; time series) to learn the spatial-temporal features from real local cortical space.<br><br>MAIN RESULTS: A benchmark RSVP EEG dataset was utilized to evaluate the performance of the proposed method, where EEG data of 63 subjects were analyzed. Compared with several state-of-the-art methods, the spatial-temporal patterns of proposed method were more consistent with P300, and the proposed method can provide significantly better classification performance.<br><br>SIGNIFICANCE: The ensemble model in this study was end-to-end optimized, which can avoid error accumulation. The sub models optimized by gradient boosting theory can extract discriminant information complementarily and non-redundantly.}, }
@article {pmid36745911, year = {2023}, author = {Adhikary, S and Jain, K and Saha, B and Chowdhury, D}, title = {Optimized EEG Based Mood Detection with Signal Processing and Deep Neural Networks for Brain-Computer Interface.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/acb942}, pmid = {36745911}, issn = {2057-1976}, abstract = {Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration. Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.}, }
@article {pmid36743394, year = {2022}, author = {Song, M and Huang, Y and Visser, HJ and Romme, J and Liu, YH}, title = {An Energy-Efficient and High-Data-Rate IR-UWB Transmitter for Intracortical Neural Sensing Interfaces.}, journal = {IEEE journal of solid-state circuits}, volume = {57}, number = {12}, pages = {3656-3668}, pmid = {36743394}, issn = {0018-9200}, abstract = {This paper presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces. A 3-dimensional (3-D) hybrid impulse modulation that comprises phase shift keying (PSK), pulse position modulation (PPM) and pulse amplitude modulation (PAM) is proposed to increase modulation order without significantly increasing the demodulation requirement, thus leading to a high data rate of 1.66 Gbps and an increased air-transmission range. Operating in 6 - 9 GHz UWB band, the presented transmitter (TX) supports the proposed hybrid modulation with a high energy efficiency of 5.8 pJ/bit and modulation quality (EVM< -21 dB). A low-noise injection-locked ring oscillator supports 8-PSK with a phase error of 2.6°. A calibration free delay generator realizes a 4-PPM with only 115 μW and avoids potential cross-modulation between PPM and PSK. A switch-cap power amplifier with an asynchronous pulse-shaping performs 4-PAM with high energy efficiency and linearity. The TX is implemented in 28 nm CMOS technology, occupying 0.155mm[2] core area. The wireless module including a printed monopole antenna has a module area of only 1.05 cm[2]. The transmitter consumes in total 9.7 mW when transmitting -41.3 dBm/MHz output power. The wireless telemetry module has been validated ex-vivo with a 15-mm multi-layer porcine tissue, and achieves a communication (air) distance up to 15 cm, leading to at least 16× improvement in distance-moralized energy efficiency of 45 pJ/bit/meter compared to state-of-the-art.}, }
@article {pmid36741783, year = {2022}, author = {Shibu, CJ and Sreedharan, S and Arun, KM and Kesavadas, C and Sitaram, R}, title = {Explainable artificial intelligence model to predict brain states from fNIRS signals.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1029784}, pmid = {36741783}, issn = {1662-5161}, abstract = {Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. Approach: We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN); and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. Main results: We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects; and (b) Motor imagery (MI), acquired from 29 subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- or deoxy-hemoglobin levels in those locations. Significance: The xAI-fNIRS system can distinguish between the brain states related to overt and covert motor imagery from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.}, }
@article {pmid36741671, year = {2023}, author = {Almosallam, W and Aljoujou, AA and Ayoubi, HR and Alzoubi, H}, title = {Evaluation of the Effect of Antihypertensive Drugs on the Values of Dental Pulp Oxygen Saturation in Hypertension Patients: A Case-Control Study.}, journal = {Cureus}, volume = {15}, number = {1}, pages = {e33245}, pmid = {36741671}, issn = {2168-8184}, abstract = {Purpose This study aimed to know about the positive or negative effect of antihypertensive drugs of different groups on the values of dental pulp oxygen saturation in hypertension patients. Materials and Methods A case-control study to evaluate the impact of the antihypertensive drugs on the values of dental pulp oxygen saturation in hypertension patients. The studied sample consisted of 40 participants, and they were distributed into two groups: Group I (n=20): Hypertension patients treated with antihypertensive drugs, and Group II (n=20): Healthy participants. A finger pulse oximeter was recorded after a rest period of 15 minutes by BCI® Advisor® vital signs monitor. The patient was then asked to use a chlorhexidine digluconate mouth rinse for five minutes, and the two dental pulp pulse oximeters for the central upper incisors were also recorded for all participants. Data were analyzed using the Mann-Whitney U test. Results The results showed that there was no significant difference between the finger pulse oximeters of the two studied groups (P-value = 0.421). The two dental pulp oxygen saturation was higher than the control group with statistically significant (P-value = 0.043, P-value = 0.002). Conclusions Within the limitation of this study, it can be concluded that antihypertensive drugs increase the dental pulp oxygen saturation in patients with hypertension who are treated with antihypertensive drugs, and thus there is a positive effect of these drugs in stimulating the dental pulp.}, }
@article {pmid36740976, year = {2023}, author = {Yao, S and Shi, S and Zhou, Q and Wang, J and Du, X and Takahata, T and Roe, AW}, title = {Functional topography of pulvinar-visual cortex networks in macaques revealed by INS-fMRI.}, journal = {The Journal of comparative neurology}, volume = {}, number = {}, pages = {}, doi = {10.1002/cne.25456}, pmid = {36740976}, issn = {1096-9861}, abstract = {The pulvinar in the macaque monkey contains three divisions: the medial pulvinar (PM), the lateral pulvinar (PL), and the inferior pulvinar (PI). Anatomical studies have shown that connections of PM are preferentially distributed to higher association areas, those of PL are biased toward the ventral visual pathway, and those of PI are biased with the dorsal visual pathway. To study functional connections of the pulvinar at mesoscale, we used a novel method called INS-fMRI (infrared neural stimulation and functional magnetic resonance imaging). This method permits studies and comparisons of multiple pulvinar networks within single animals. As previously revealed, stimulations of different sites in PL and PI produced topographically organized focal activations in visual areas V1, V2, and V3. In contrast, PM stimulation elicited little or diffuse response. The relative activations of areas V1, V2, V3A, V3d, V3v, V4, MT, and MST revealed that connections of PL are biased to ventral pathway areas, and those of PI are biased to dorsal areas. Different statistical values of activated blood-oxygen-level-dependent responses produced the same center of activation, indicating stability of connectivity; it also suggests possible dynamics of broad to focal responses from single stimulation sites. These results demonstrate that infrared neural stimulation-induced connectivity is largely consistent with previous anatomical connectivity studies, thereby demonstrating validity of our novel method. In addition, it suggests additional interpretations of functional connectivity to complement anatomical studies.}, }
@article {pmid36742108, year = {2021}, author = {Jee, S}, title = {Brain Oscillations and Their Implications for Neurorehabilitation.}, journal = {Brain &amp; NeuroRehabilitation}, volume = {14}, number = {1}, pages = {e7}, pmid = {36742108}, issn = {2383-9910}, abstract = {Neural oscillation is rhythmic or repetitive neural activities, which can be observed at all levels of the central nervous system (CNS). The large-scale oscillations measured by electroencephalography have long been used in clinical practice and may have a potential for the usage in neurorehabilitation for people with various CNS disorders. The recent advancement of computational neuroscience has opened up new opportunities to explore clinical application of the results of neural oscillatory activity analysis to evaluation and diagnosis; monitoring the rehab progress; prognostication; and personalized rehabilitation planning in neurorehabilitation. In addition, neural oscillation is catching more attention to its role as a target of noninvasive neuromodulation in neurological disorders.}, }
@article {pmid36738734, year = {2023}, author = {Cui, Q and Bi, H and Lv, Z and Wu, Q and Hua, J and Gu, B and Huo, C and Tang, M and Chen, Y and Chen, C and Chen, S and Zhang, X and Wu, Z and Lao, Z and Sheng, N and Shen, C and Zhang, Y and Wu, ZY and Jin, Z and Yang, P and Liu, H and Li, J and Bai, G}, title = {Diverse CMT2 neuropathies are linked to aberrant G3BP interactions in stress granules.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2022.12.046}, pmid = {36738734}, issn = {1097-4172}, abstract = {Complex diseases often involve the interplay between genetic and environmental factors. Charcot-Marie-Tooth type 2 neuropathies (CMT2) are a group of genetically heterogeneous disorders, in which similar peripheral neuropathology is inexplicably caused by various mutated genes. Their possible molecular links remain elusive. Here, we found that upon environmental stress, many CMT2-causing mutant proteins adopt similar properties by entering stress granules (SGs), where they aberrantly interact with G3BP and integrate into SG pathways. For example, glycyl-tRNA synthetase (GlyRS) is translocated from the cytoplasm into SGs upon stress, where the mutant GlyRS perturbs the G3BP-centric SG network by aberrantly binding to G3BP. This disrupts SG-mediated stress responses, leading to increased stress vulnerability in motoneurons. Disrupting this aberrant interaction rescues SG abnormalities and alleviates motor deficits in CMT2D mice. These findings reveal a stress-dependent molecular link across diverse CMT2 mutants and provide a conceptual framework for understanding genetic heterogeneity in light of environmental stress.}, }
@article {pmid36736668, year = {2023}, author = {Deng, J and Sun, J and Lu, S and Yue, K and Liu, W and Shi, H and Zou, L}, title = {Exploring neural activity in inflammatory bowel diseases using functional connectivity and DKI-fMRI fusion.}, journal = {Behavioural brain research}, volume = {}, number = {}, pages = {114325}, doi = {10.1016/j.bbr.2023.114325}, pmid = {36736668}, issn = {1872-7549}, abstract = {Although MRI has made considerable progress in Inflammatory bowel disease (IBD), most studies have concentrated on data information from a single modality, and a better understanding of the interplay between brain function and structure, as well as appropriate clinical aids to diagnosis, is required. We calculated functional connectivity through fMRI time series using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 27 IBD patients and 29 healthy controls. Through the DKI data of each subject, its unique structure map is obtained, and the relevant indicators are projected onto the structure map corresponding to each subject by using the graph Fourier transform in the grasp signal processing (GSP) technology. After the features are optimized, a classical support vector machine is used to classify the features. IBD patients have altered functional connectivity in the default mode network (DMN) and subcortical network (SCN). At the same time, compared with the traditional brain network analysis, in the test of some indicators, the average classification accuracy produced by the framework method is 12.73% higher than that of the traditional analysis method. This paper found that the brain network structure of IBD patients in DMN and SCN has changed. Simultaneously, the application of GSP technology to fuse functional information and structural information is superior to the traditional framework in classification, providing a new perspective for subsequent clinical auxiliary diagnosis.}, }
@article {pmid36736571, year = {2023}, author = {Yan, K and Tao, R and Huang, X and Zhang, E}, title = {Influence of advisees' facial feedback on subsequent advice-giving by advisors: Evidence from the behavioral and neurophysiological approach.}, journal = {Biological psychology}, volume = {}, number = {}, pages = {108506}, doi = {10.1016/j.biopsycho.2023.108506}, pmid = {36736571}, issn = {1873-6246}, abstract = {Previous work has demonstrated the interpersonal implications of advisees' decisions (acceptance or rejection) on advisors' advice-giving behavior in subsequent exchanges. Here, using an ERP technique, we investigated how advisees' facial feedback (smiling, neutral, or frowning) accompanying their decisions (acceptance or rejection) influenced advisors' feedback evaluation from advisees and their advice-giving in subsequent exchanges. Behaviorally, regardless of whether the advice was accepted or rejected, advisors who received smiling-expression feedback would show higher willingness rates in subsequent advice-giving decisions, while advisors who received frowning-expression feedback would show lower willingness rates. On the neural level, in the feedback evaluation stage, the FRN and P3 responses were not sensitive to facial feedback. In contrast, frowning-expression feedback elicited a larger LPC amplitude than neutral- and smiling-expression feedback, regardless of whether the advice was accepted or rejected. In the advice decision stage, advisors who received neutral-expression feedback showed a larger N2 in making decisions than advisors who received frowning-expression feedback only after the advice was rejected. Additionally, Advisors who received smiling- and neutral-expression feedback showed a larger P3 in making decisions than advisors who received frowning-expression feedback only after the advice was accepted. In sum, the current findings extended previous research findings by showing that the effect of advisees' facial expressions on the advisors' advice-giving existed in multiple stages, including both the feedback evaluation stage and the advice decision stage.}, }
@article {pmid36736001, year = {2023}, author = {Mao, J and Qiu, S and Wei, W and He, H}, title = {Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {161}, number = {}, pages = {65-82}, doi = {10.1016/j.neunet.2023.01.009}, pmid = {36736001}, issn = {1879-2782}, abstract = {Rapid Serial Visual Presentation (RSVP) based Brain-Computer Interface (BCI) facilities the high-throughput detection of rare target images by detecting evoked event-related potentials (ERPs). At present, the decoding accuracy of the RSVP-based BCI system limits its practical applications. This study introduces eye movements (gaze and pupil information), referred to as EYE modality, as another useful source of information to combine with EEG-based BCI and forms a novel target detection system to detect target images in RSVP tasks. We performed an RSVP experiment, recorded the EEG signals and eye movements simultaneously during a target detection task, and constructed a multi-modal dataset including 20 subjects. Also, we proposed a cross-modal guiding and fusion network to fully utilize EEG and EYE modalities and fuse them for better RSVP decoding performance. In this network, a two-branch backbone was built to extract features from these two modalities. A Cross-Modal Feature Guiding (CMFG) module was proposed to guide EYE modality features to complement the EEG modality for better feature extraction. A Multi-scale Multi-modal Reweighting (MMR) module was proposed to enhance the multi-modal features by exploring intra- and inter-modal interactions. And, a Dual Activation Fusion (DAF) was proposed to modulate the enhanced multi-modal features for effective fusion. Our proposed network achieved a balanced accuracy of 88.00% (±2.29) on the collected dataset. The ablation studies and visualizations revealed the effectiveness of the proposed modules. This work implies the effectiveness of introducing the EYE modality in RSVP tasks. And, our proposed network is a promising method for RSVP decoding and further improves the performance of RSVP-based target detection systems.}, }
@article {pmid36733372, year = {2023}, author = {Gams, A and Naik, GR}, title = {Editorial: Neurorobotics explores gait movement in the sporting community.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1127994}, pmid = {36733372}, issn = {1662-5218}, }
@article {pmid36731812, year = {2023}, author = {Soroush, PZ and Herff, C and Ries, SK and Shih, JJ and Schultz, T and Krusienski, DJ}, title = {The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119913}, doi = {10.1016/j.neuroimage.2023.119913}, pmid = {36731812}, issn = {1095-9572}, abstract = {Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech modes, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate the existence of a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.}, }
@article {pmid36731770, year = {2023}, author = {Pan, L and Ping, A and Schriver, KE and Roe, AW and Zhu, J and Xu, K}, title = {Infrared neural stimulation in human cerebral cortex.}, journal = {Brain stimulation}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.brs.2023.01.1678}, pmid = {36731770}, issn = {1876-4754}, abstract = {BACKGROUND: Modulation of brain circuits by electrical stimulation has led to exciting and powerful therapies for diseases such as Parkinson's. Because human brain organization is based in mesoscale (millimeter-scale) functional nodes, having a method that can selectively target such nodes could enable more precise, functionally specific stimulation therapies. Infrared Neural Stimulation (INS) is an emerging stimulation technology that stimulates neural tissue via delivery of tiny heat pulses. In nonhuman primates, this optical method provides focal intensity-dependent stimulation of the brain without tissue damage. However, whether INS application to the human central nervous system (CNS) is similarly effective is unknown.<br><br>OBJECTIVE: To examine the effectiveness of INS on human cerebral cortex in intraoperative setting and to evaluate INS damage threshholds.<br><br>METHODS: Five epileptic subjects undergoing standard lobectomy for epilepsy consented to this study. Cortical response to INS was assessed by intrinsic signal optical imaging (OI, a method that detects changes in tissue reflectance due to neuronal activity). A custom integrated INS and OI system was developed specifically for short-duration INS and OI acquisition during surgical procedures. Single pulse trains of INS with intensities from 0.2 to 0.8 J/cm[2] were delivered to the somatosensory cortex and responses were recorded via optical imaging. Following tissue resection, histological analysis was conducted to evaluate damage threshholds.<br><br>RESULTS: As assessed by OI, and similar to results in monkeys, INS induced responses in human cortex were highly focal (millimeter sized) and led to relative suppression of nearby cortical sites. Intensity dependence was observed at both stimulated and functionally connected sites. Histological analysis of INS-stimulated human cortical tissue provided damage threshold estimates.<br><br>CONCLUSION: This is the first study demonstrating application of INS to human CNS and shows feasibility for stimulating single cortical nodes and associated sites and provided INS damage threshold estimates for cortical tissue. Our results suggest that INS is a promising tool for stimulation of functionally selective mesoscale circuits in the human brain, and may lead to advances in the future of precision medicine.}, }
@article {pmid36731636, year = {2023}, author = {Jin, J and Chen, X and Zhang, D and Liang, Z}, title = {Editorial for the Special Issue "Visual Evoked Brain Computer Interface Studies".}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109806}, doi = {10.1016/j.jneumeth.2023.109806}, pmid = {36731636}, issn = {1872-678X}, }
@article {pmid36729587, year = {2023}, author = {Rimbert, S and Lelarge, J and Guerci, P and Bidgoli, SJ and Meistelman, C and Cheron, G and Cebolla Alvarez, AM and Schmartz, D}, title = {Detection of Motor Cerebral Activity After Median Nerve Stimulation During General Anesthesia (STIM-MOTANA): Protocol for a Prospective Interventional Study.}, journal = {JMIR research protocols}, volume = {12}, number = {}, pages = {e43870}, doi = {10.2196/43870}, pmid = {36729587}, issn = {1929-0748}, abstract = {BACKGROUND: Accidental awareness during general anesthesia (AAGA) is defined as an unexpected awareness of the patient during general anesthesia. This phenomenon occurs in 1%-2% of high-risk practice patients and can cause physical suffering and psychological after-effects, called posttraumatic stress disorder. In fact, no monitoring techniques are satisfactory enough to effectively prevent AAGA; therefore, new alternatives are needed. Because the first reflex for a patient during an AAGA is to move, but cannot do so because of the neuromuscular blockers, we believe that it is possible to design a brain-computer interface (BCI) based on the detection of movement intention to warn the anesthetist. To do this, we propose to describe and detect the changes in terms of motor cortex oscillations during general anesthesia with propofol, while a median nerve stimulation is performed. We believe that our results could enable the design of a BCI based on median nerve stimulation, which could prevent AAGA.<br><br>OBJECTIVE: To our knowledge, no published studies have investigated the detection of electroencephalographic (EEG) patterns in relation to peripheral nerve stimulation over the sensorimotor cortex during general anesthesia. The main objective of this study is to describe the changes in terms of event-related desynchronization and event-related synchronization modulations, in the EEG signal over the motor cortex during general anesthesia with propofol while a median nerve stimulation is performed.<br><br>METHODS: STIM-MOTANA is an interventional and prospective study conducted with patients scheduled for surgery under general anesthesia, involving EEG measurements and median nerve stimulation at two different times: (1) when the patient is awake before surgery (2) and under general anesthesia. A total of 30 patients will receive surgery under complete intravenous anesthesia with a target-controlled infusion pump of propofol.<br><br>RESULTS: The changes in event-related desynchronization and event-related synchronization during median nerve stimulation according to the various propofol concentrations for 30 patients will be analyzed. In addition, we will apply 4 different offline machine learning algorithms to detect the median nerve stimulation at the cerebral level. Recruitment began in December 2022. Data collection is expected to conclude in June 2024.<br><br>CONCLUSIONS: STIM-MOTANA will be the first protocol to investigate median nerve stimulation cerebral motor effect during general anesthesia for the detection of intraoperative awareness. Based on strong practical and theoretical scientific reasoning from our previous studies, our innovative median nerve stimulation-based BCI would provide a way to detect intraoperative awareness during general anesthesia.<br><br>TRIAL REGISTRATION: Clinicaltrials.gov NCT05272202; https://clinicaltrials.gov/ct2/show/NCT05272202.<br><br>PRR1-10.2196/43870.}, }
@article {pmid36729246, year = {2023}, author = {Knopf, S and Frahm, N and M Pfotenhauer, S}, title = {How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation.}, journal = {Science and engineering ethics}, volume = {29}, number = {1}, pages = {4}, pmid = {36729246}, issn = {1471-5546}, abstract = {Like many ethics debates surrounding emerging technologies, neuroethics is increasingly concerned with the private sector. Here, entrepreneurial visions and claims of how neurotechnology innovation will revolutionize society-from brain-computer-interfaces to neural enhancement and cognitive phenotyping-are confronted with public and policy concerns about the risks and ethical challenges related to such innovations. But while neuroethics frameworks have a longer track record in public sector research such as the U.S. BRAIN Initiative, much less is known about how businesses-and especially start-ups-address ethics in tech development. In this paper, we investigate how actors in the field frame and enact ethics as part of their innovative R&amp;D processes and business models. Drawing on an empirical case study on direct-to-consumer (DTC) neurotechnology start-ups, we find that actors engage in careful boundary-work to anticipate and address public critique of their technologies, which allows them to delineate a manageable scope of their ethics integration. In particular, boundaries are drawn around four areas: the technology's actual capability, purpose, safety and evidence-base. By drawing such lines of demarcation, we suggest that start-ups make their visions of ethical neurotechnology in society more acceptable, plausible and desirable, favoring their innovations while at the same time assigning discrete responsibilities for ethics. These visions establish a link from the present into the future, mobilizing the latter as promissory place where a technology's benefits will materialize and to which certain ethical issues can be deferred. In turn, the present is constructed as a moment in which ethical engagement could be delegated to permissive regulatory standards and scientific authority. Our empirical tracing of the construction of 'ethical realities' in and by start-ups offers new inroads for ethics research and governance in tech industries beyond neurotechnology.}, }
@article {pmid36726940, year = {2023}, author = {Liu, Y and Xu, S and Yang, Y and Zhang, K and He, E and Liang, W and Luo, J and Wu, Y and Cai, X}, title = {Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review.}, journal = {Microsystems &amp; nanoengineering}, volume = {9}, number = {}, pages = {13}, pmid = {36726940}, issn = {2055-7434}, abstract = {A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.}, }
@article {pmid36726556, year = {2022}, author = {Hossain, KM and Islam, MA and Hossain, S and Nijholt, A and Ahad, MAR}, title = {Status of deep learning for EEG-based brain-computer interface applications.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1006763}, pmid = {36726556}, issn = {1662-5188}, abstract = {In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.}, }
@article {pmid36723288, year = {2023}, author = {Yang, T and Wang, SC and Ye, L and Maimaitiyiming, Y and Naranmandura, H}, title = {Targeting Viral Proteins for Restraining SARS-CoV-2: Focusing Lens on Viral Proteins Beyond Spike for Discovering New Drug Targets.}, journal = {Expert opinion on drug discovery}, volume = {}, number = {}, pages = {}, doi = {10.1080/17460441.2023.2175812}, pmid = {36723288}, issn = {1746-045X}, abstract = {INTRODUCTION: Emergence of highly infectious SARS-CoV-2 variants are reducing protection provided by the current vaccines, requiring constant updates in antiviral approaches. As a member of the Coronaviridae family, SARS-CoV-2 encodes four structural and sixteen nonstructural proteins which participate in various aspects of the viral life cycle including genome replication and transcription, virion assembly, release and entry into cells, as well as compromising host cellular defenses. As alien proteins to host cells, many viral proteins represent potential targets for combating the SARS-CoV-2.<br><br>AREAS COVERED: Based on literature from PubMed and Web of Science databases, the authors summarize the typical characteristics of SARS-CoV-2 from the whole viral particle to the individual viral proteins as well as their corresponding functions in virus life cycle. The authors also discuss the potential and emerging targeted interventions to curb virus replication and spread in detail to provide unique insights into the rapidly spreading SARS-CoV-2 infection and countermeasures against it.<br><br>EXPERT OPINION: Our comprehensive analysis highlights the rationale and need to focus on non-spike viral proteins that are less mutated but has important functions. Examples of this include: structural proteins (e.g., nucleocapsid protein, envelope protein) and extensively-concerned nonstructural proteins (e.g., NSP3, NSP5, NSP12) as well as the ones with relatively less attention (e.g., NSP1, NSP10, NSP14 and NSP16), for developing novel drugs to overcome resistance of SARS-CoV-2 variants to preexisting vaccines and antibody-based treatments.}, }
@article {pmid36721006, year = {2023}, author = {Li, Z and Zheng, Y and Diao, X and Li, R and Sun, N and Xu, Y and Li, X and Duan, S and Gong, W and Si, K}, title = {Robust and adjustable dynamic scattering compensation for high-precision deep tissue optogenetics.}, journal = {Communications biology}, volume = {6}, number = {1}, pages = {128}, doi = {10.1038/s42003-023-04487-w}, pmid = {36721006}, issn = {2399-3642}, abstract = {The development of high-precision optogenetics in deep tissue is limited due to the strong optical scattering induced by biological tissue. Although various wavefront shaping techniques have been developed to compensate the scattering, it is still a challenge to non-invasively characterize the dynamic scattered optical wavefront inside the living tissue. Here, we present a non-invasive scattering compensation system with fast multidither coherent optical adaptive technique (fCOAT), which allows the rapid wavefront correction and stable focusing in dynamic scattering medium. We achieve subcellular-resolution focusing through 500-μm-thickness brain slices, or even three pieces overlapped mouse skulls after just one iteration with a 589 nm CW laser. Further, focusing through dynamic scattering medium such as live rat ear is also successfully achieved. The formed focus can maintain longer than 60 s, which satisfies the requirements of stable optogenetics manipulation. Moreover, the focus size is adjustable from subcellular level to tens of microns to freely match the various manipulation targets. With the specially designed fCOAT system, we successfully achieve single-cellular optogenetic manipulation through the brain tissue, with a stimulation efficiency enhancement up to 300% compared with that of the speckle.}, }
@article {pmid36720854, year = {2023}, author = {Duan, J and Xu, P and Zhang, H and Luan, X and Yang, J and He, X and Mao, C and Shen, DD and Ji, Y and Cheng, X and Jiang, H and Jiang, Y and Zhang, S and Zhang, Y and Xu, HE}, title = {Mechanism of hormone and allosteric agonist mediated activation of follicle stimulating hormone receptor.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {519}, pmid = {36720854}, issn = {2041-1723}, abstract = {Follicle stimulating hormone (FSH) is an essential glycoprotein hormone for human reproduction, which functions are mediated by a G protein-coupled receptor, FSHR. Aberrant FSH-FSHR signaling causes infertility and ovarian hyperstimulation syndrome. Here we report cryo-EM structures of FSHR in both inactive and active states, with the active structure bound to FSH and an allosteric agonist compound 21 f. The structures of FSHR are similar to other glycoprotein hormone receptors, highlighting a conserved activation mechanism of hormone-induced receptor activation. Compound 21 f formed extensive interactions with the TMD to directly activate FSHR. Importantly, the unique residue H615[7.42] in FSHR plays an essential role in determining FSHR selectivity for various allosteric agonists. Together, our structures provide a molecular basis of FSH and small allosteric agonist-mediated FSHR activation, which could inspire the design of FSHR-targeted drugs for the treatment of infertility and controlled ovarian stimulation for in vitro fertilization.}, }
@article {pmid36720164, year = {2023}, author = {Li, Z and Zhang, G and Wang, L and Wei, J and Dang, J}, title = {Emotion recognition using spatial-temporal EEG features through convolutional graph attention network.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb79e}, pmid = {36720164}, issn = {1741-2552}, abstract = {OBJECTIVE: Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is of great significance for realizing emotional brain computer interaction and improving machine intelligence.<br><br>APPROACH: In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model.<br><br>MAIN RESULTS: We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11&#xb1;0.83% and 94.83&#xb1;3.41% in subject-dependent and subject-independent experiments on SEED dataset, and achieved an accuracy of 91.19&#xb1;1.24% and 92.03&#xb1;4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art (SOTA) performance on cross-subject emotion recognition task for both datasets. In addition, we gained an insight into the proposed frame by both the ablation experiments and the analysis of spatial patterns of FC and DE features.<br><br>SIGNIFICANCE: All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.}, }
@article {pmid36720162, year = {2023}, author = {Remakanthakurup Sindhu, K and Ngo, D and Ombao, H and Olaya, JE and Shrey, DW and Lopour, BA}, title = {A novel method for dynamically altering the surface area of intracranial EEG electrodes.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb79f}, pmid = {36720162}, issn = {1741-2552}, abstract = {Intracranial EEG (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain. We first present a theoretical model and an in vitro validation of the method. We then report the results of an in vivo implementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e., epileptic spikes. We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike. Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.}, }
@article {pmid36719563, year = {2023}, author = {Dong, Y and Wang, L and Li, M}, title = {Applying correlation analysis to electrode optimization in source domain.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36719563}, issn = {1741-0444}, abstract = {In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.}, }
@article {pmid36716553, year = {2023}, author = {Fan, C and Zha, R and Liu, Y and Wei, Z and Wang, Y and Song, H and Lv, W and Ren, J and Hong, W and Gou, H and Zhang, P and Chen, Y and Zhou, Y and Pan, Y and Zhang, X}, title = {Altered white matter functional network in nicotine addiction.}, journal = {Psychiatry research}, volume = {321}, number = {}, pages = {115073}, doi = {10.1016/j.psychres.2023.115073}, pmid = {36716553}, issn = {1872-7123}, abstract = {Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.}, }
@article {pmid36716494, year = {2023}, author = {Delisle-Rodriguez, D and Silva, L and Bastos Filho, TF}, title = {EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb73b}, pmid = {36716494}, issn = {1741-2552}, abstract = {OBJECTIVE: This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.<br><br>METHOD: After filtering the raw EEG, a two-step method for spatial feature extraction by using the Riemannian Covariance Matrices (RCM) method and Common Spatial Patterns (CSP) is proposed here. It uses electroencephalogram (EEG) data from trials providing feedback, in an intermediate step composed of both kth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.<br><br>RESULTS: The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.<br><br>SIGNIFICANCE: Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.}, }
@article {pmid36711591, year = {2023}, author = {Willett, F and Kunz, E and Fan, C and Avansino, D and Wilson, G and Choi, EY and Kamdar, F and Hochberg, LR and Druckmann, S and Shenoy, KV and Henderson, JM}, title = {A high-performance speech neuroprosthesis.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.01.21.524489}, pmid = {36711591}, abstract = {Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text [1,2] or sound [3,4] .Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary [1â€"5] . Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI [2]) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI [6] and begins to approach the speed of natural conversation (160 words per minute [7]). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.}, }
@article {pmid36711163, year = {2023}, author = {Cho, YK and Koh, CS and Lee, Y and Park, M and Kim, TJ and Jung, HH and Chang, JW and Jun, SB}, title = {Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle.}, journal = {Biomedical engineering letters}, volume = {13}, number = {1}, pages = {85-95}, pmid = {36711163}, issn = {2093-985X}, abstract = {Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.}, }
@article {pmid36711161, year = {2023}, author = {Valencia, D and Alimohammad, A}, title = {Partially binarized neural networks for efficient spike sorting.}, journal = {Biomedical engineering letters}, volume = {13}, number = {1}, pages = {73-83}, pmid = {36711161}, issn = {2093-985X}, abstract = {While brain-implantable neural spike sorting can be realized using efficient algorithms, the presence of noise may make it difficult to maintain high-peformance sorting using conventional techniques. In this article, we explore the use of partially binarized neural networks (PBNNs), to the best of our knowledge for the first time, for sorting of neural spike feature vectors. It is shown that compared to the waveform template-based methods, PBNNs offer robust spike sorting over various datasets and noise levels. The ASIC implementation of the PBNN-based spike sorting system in a standard 180-nm CMOS process is presented. The post place and route simulations results show that the synthesized PBNN consumes only 0.59 μ W of power from a 1.8 V supply while operating at 24 kHz and occupies 0.15 mm 2 of silicon area. It is shown that the designed PBNN-based spike sorting system not only offers comparable accuracy to the state-of-the-art spike sorting systems over various noise levels and datasets, it also occupies a smaller silicon area and consumes less power and energy. This makes PBNNs a viable alternative towards the implementation of brain-implantable spike sorting systems.}, }
@article {pmid36711153, year = {2022}, author = {Sohn, WJ and Lim, J and Wang, PT and Pu, H and Malekzadeh-Arasteh, O and Shaw, SJ and Armacost, M and Gong, H and Kellis, S and Andersen, RA and Liu, CY and Heydari, P and Nenadic, Z and Do, AH}, title = {Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1075971}, pmid = {36711153}, issn = {1662-4548}, abstract = {INTRODUCTION: Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback.<br><br>METHODS: A custom programmable direct cortical stimulator (DCS) capable of eliciting artificial sensorimotor response was integrated into an embedded BCI system to form a safe, independent, wireless, and battery powered testbed to explore BD-BCI concepts at a low cost. The BD-BCI stimulator output was tested in phantom brain tissue by assessing its ability to deliver electrical stimulation equivalent to an FDA-approved commercial electrical cortical stimulator. Subsequently, the stimulator was tested in an epilepsy patient with subcortical electrocorticographic (ECoG) implants covering the sensorimotor cortex to assess its ability to elicit equivalent responses as the FDA-approved counterpart. Additional safety features (impedance monitoring, artifact mitigation, and passive and active charge balancing mechanisms) were also implemeneted and tested in phantom brain tissue. Finally, concurrent operation with interleaved stimulation and BCI decoding was tested in a phantom brain as a proof-of-concept operation of BD-BCI system.<br><br>RESULTS: The benchtop prototype BD-BCI stimulator's basic output features (current amplitude, pulse frequency, pulse width, train duration) were validated by demonstrating the output-equivalency to an FDA-approved commercial cortical electrical stimulator (R [2] > 0.99). Charge-neutral stimulation was demonstrated with pulse-width modulation-based correction algorithm preventing steady state voltage deviation. Artifact mitigation achieved a 64.5% peak voltage reduction. Highly accurate impedance monitoring was achieved with R [2] > 0.99 between measured and actual impedance, which in-turn enabled accurate charge density monitoring. An online BCI decoding accuracy of 93.2% between instructional cues and decoded states was achieved while delivering interleaved stimulation. The brain stimulation mapping via ECoG grids in an epilepsy patient showed that the two stimulators elicit equivalent responses.<br><br>SIGNIFICANCE: This study demonstrates clinical validation of a fully-programmable electrical stimulator, integrated into an embedded BCI system. This low-cost BD-BCI system is safe and readily applicable as a testbed for BD-BCI research. In particular, it provides an all-inclusive hardware platform that approximates the limitations in a near-future implantable BD-BCI. This successful benchtop/human validation of the programmable electrical stimulator in a BD-BCI system is a critical milestone toward fully-implantable BD-BCI systems.}, }
@article {pmid36711141, year = {2022}, author = {Li, H and Liu, M and Yu, X and Zhu, J and Wang, C and Chen, X and Feng, C and Leng, J and Zhang, Y and Xu, F}, title = {Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1097660}, pmid = {36711141}, issn = {1662-4548}, abstract = {BACKGROUND: Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients.<br><br>METHODS: According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.<br><br>RESULTS: The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.<br><br>CONCLUSION: The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.}, }
@article {pmid36710855, year = {2022}, author = {Sajno, E and Bartolotta, S and Tuena, C and Cipresso, P and Pedroli, E and Riva, G}, title = {Machine learning in biosignals processing for mental health: A narrative review.}, journal = {Frontiers in psychology}, volume = {13}, number = {}, pages = {1066317}, pmid = {36710855}, issn = {1664-1078}, abstract = {Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.}, }
@article {pmid36709613, year = {2023}, author = {Cai, J and Xie, M and Zhao, L and Li, X and Liang, S and Deng, W and Guo, W and Ma, X and Sham, PC and Wang, Q and Li, T}, title = {White matter changes and its relationship with clinical symptom in medication-naive first-episode early onset schizophrenia.}, journal = {Asian journal of psychiatry}, volume = {82}, number = {}, pages = {103482}, doi = {10.1016/j.ajp.2023.103482}, pmid = {36709613}, issn = {1876-2026}, abstract = {Previous studies have highlighted the role of white matter (WM) alterations as biomarkers of the disease state and prognosis of schizophrenia. However, less is known about WM abnormalities in the rarely occurring adolescent early onset schizophrenia (EOS). In this study, T1-weighted and diffusion-weighted images were collected in 56 medication-naive first-episode participants with EOS and 43 healthy controls (HCs). Using Tract-based Spatial Statistics, we calculate case-control differences in scalar diffusion measures, i.e. fractional anisotropy (FA) and mean diffusivity (MD), and investigated their association with clinical feature in participants with EOS. Compared with HCs, decreased MD was found in EOS group most notably in the inferior longitudinal fasciculus, anterior thalamic radiation, inferior fronto-occipital fasciculus and corticospinal tract in the right hemisphere. No significant difference was found in FA between these two groups. The FA values of the forceps minor and the right superior longitudinal fasciculus were suggested to be related to the severity of clinical symptom in participants with EOS. These results provide clues about the neural basis of schizophrenia and a potential biomarker for clinical studies.}, }
@article {pmid36707885, year = {2023}, author = {Angerhöfer, C and Vermehren, M and Colucci, A and Nann, M and Koßmehl, P and Niedeggen, A and Kim, WS and Chang, WK and Paik, NJ and Hömberg, V and Soekadar, SR}, title = {The Berlin Bimanual Test for Tetraplegia (BeBiTT): development, psychometric properties, and sensitivity to change in assistive hand exoskeleton application.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {17}, pmid = {36707885}, issn = {1743-0003}, support = {759370/ERC_/European Research Council/International ; }, abstract = {BACKGROUND: Assistive hand exoskeletons are promising tools to restore hand function after cervical spinal cord injury (SCI) but assessing their specific impact on bimanual hand and arm function is limited due to lack of reliable and valid clinical tests. Here, we introduce the Berlin Bimanual Test for Tetraplegia (BeBiTT) and demonstrate its psychometric properties and sensitivity to assistive hand exoskeleton-related improvements in bimanual task performance.<br><br>METHODS: Fourteen study participants with subacute cervical SCI performed the BeBiTT unassisted (baseline). Thereafter, participants repeated the BeBiTT while wearing a brain/neural hand exoskeleton (B/NHE) (intervention). Online control of the B/NHE was established via a hybrid sensorimotor rhythm-based brain-computer interface (BCI) translating electroencephalographic (EEG) and electrooculographic (EOG) signals into open/close commands. For reliability assessment, BeBiTT scores were obtained by four independent observers. Besides internal consistency analysis, construct validity was assessed by correlating baseline BeBiTT scores with the Spinal Cord Independence Measure III (SCIM III) and Quadriplegia Index of Function (QIF). Sensitivity to differences in bimanual task performance was assessed with a bootstrapped paired t-test.<br><br>RESULTS: The BeBiTT showed excellent interrater reliability (intraclass correlation coefficients&#x2009;>&#x2009;0.9) and internal consistency (&#x3b1;&#x2009;=&#x2009;0.91). Validity of the BeBiTT was evidenced by strong correlations between BeBiTT scores and SCIM III as well as QIF. Wearing a B/NHE (intervention) improved the BeBiTT score significantly (p&#x2009;<&#x2009;0.05) with high effect size (d&#x2009;=&#x2009;1.063), documenting high sensitivity to intervention-related differences in bimanual task performance.<br><br>CONCLUSION: The BeBiTT is a reliable and valid test for evaluating bimanual task performance in persons with tetraplegia, suitable to assess the impact of assistive hand exoskeletons on bimanual function.}, }
@article {pmid36706879, year = {2023}, author = {Li, H and Shen, S and Yu, K and Wang, H and Fu, J}, title = {Construction of porous structure-based carboxymethyl chitosan/sodium alginate/tea polyphenols for wound dressing.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {123404}, doi = {10.1016/j.ijbiomac.2023.123404}, pmid = {36706879}, issn = {1879-0003}, abstract = {Polysaccharide-based materials with porous structure were selected as the basic skeleton to prepare a flexible and biodegradable wound dressing. The carboxymethyl chitosan/sodium alginate/tea polyphenols (CC/SA/TP) with two-layer porous structure exhibits a variety of performances. The specific combined structure with ordered and lamellar porous structure was constructed by high-speed homogenized foaming, Ca[2+] crosslinking and two-step freeze-drying methods. Moreover, the CC/SA/TP porous structure owns a better shape retention and recovery because of the 3D network with "egg-box" structure formed by impregnation. Tea polyphenols are efficiently encapsulated into porous structure and released in a sustained pattern. After storing for 60 days, the CC/SA/TP porous structure still exhibits great suitable water vapor transmittance, efficient antibacterial activity and ultrarapid antioxidant activity. Meanwhile, the relatively low differential blood clotting index (BCI) and cytotoxicity of the CC/SA/TP porous structure indicate that it possesses the possibility for adjusting and controlling wound bleeding. The test results reveal that the CC/SA/TP porous structure might be expected to play a great potential role in biomedical applications of wound dressing.}, }
@article {pmid36705845, year = {2023}, author = {Zhao, ZD and Zhang, L and Xiang, X and Kim, D and Li, H and Cao, P and Shen, WL}, title = {Neurocircuitry of Predatory Hunting.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1007/s12264-022-01018-1}, pmid = {36705845}, issn = {1995-8218}, abstract = {Predatory hunting is an important type of innate behavior evolutionarily conserved across the animal kingdom. It is typically composed of a set of sequential actions, including prey search, pursuit, attack, and consumption. This behavior is subject to control by the nervous system. Early studies used toads as a model to probe the neuroethology of hunting, which led to the proposal of a sensory-triggered release mechanism for hunting actions. More recent studies have used genetically-trackable zebrafish and rodents and have made breakthrough discoveries in the neuroethology and neurocircuits underlying this behavior. Here, we review the sophisticated neurocircuitry involved in hunting and summarize the detailed mechanism for the circuitry to encode various aspects of hunting neuroethology, including sensory processing, sensorimotor transformation, motivation, and sequential encoding of hunting actions. We also discuss the overlapping brain circuits for hunting and feeding and point out the limitations of current studies. We propose that hunting is an ideal behavioral paradigm in which to study the neuroethology of motivated behaviors, which may shed new light on epidemic disorders, including binge-eating, obesity, and obsessive-compulsive disorders.}, }
@article {pmid36704636, year = {2023}, author = {Lyu, X and Ding, P and Li, S and Dong, Y and Su, L and Zhao, L and Gong, A and Fu, Y}, title = {Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {1}, pages = {105-118}, pmid = {36704636}, issn = {1871-4080}, abstract = {Existing brain-computer interface (BCI) research has made great progress in improving the accuracy and information transfer rate (ITR) of BCI systems. However, the practicability of BCI is still difficult to achieve. One of the important reasons for this difficulty is that human factors are not fully considered in the research and development of BCI. As a result, BCI systems have not yet reached users' expectations. In this study, we investigate a BCI system of motor imagery for lower limb synchronous rehabilitation as an example. From the perspective of human factors engineering of BCI, a comprehensive evaluation method of BCI system development is proposed based on the concept of human-centered design and evaluation. Subjects' satisfaction ratings for BCI sensors, visual analog scale (VAS), subjects' satisfaction rating of the BCI system, and the mental workload rating for subjects manipulating the BCI system, as well as interview/follow-up comprehensive evaluation of motor imagery of BCI (MI-BCI) system satisfaction were used. The methods and concepts proposed in this study provide useful insights for the design of personalized MI-BCI. We expect that the human factors engineering of BCI could be applied to the design and satisfaction evaluation of MI-BCI, so as to promote the practical application of this kind of BCI.}, }
@article {pmid36704625, year = {2023}, author = {Cui, Z and Lin, J and Fu, X and Zhang, S and Li, P and Wu, X and Wang, X and Chen, W and Zhu, S and Li, Y}, title = {Construction of the dynamic model of SCI rehabilitation using bidirectional stimulation and its application in rehabilitating with BCI.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {1}, pages = {169-181}, pmid = {36704625}, issn = {1871-4080}, abstract = {UNLABELLED: Patients with complete spinal cord injury have a complete loss of motor and sensory functions below the injury plane, leading to a complete loss of function of the nerve pathway in the injured area. Improving the microenvironment in the injured area of patients with spinal cord injury, promoting axon regeneration of the nerve cells is challenging research fields. The brain-computer interface rehabilitation system is different from the other rehabilitation techniques. It can exert bidirectional stimulation on the spinal cord injury area, and can make positively rehabilitation effects of the patient with complete spinal cord injury. A dynamic model was constructed for the patient with spinal cord injury under-stimulation therapy, and the mechanism of the brain-computer interface in rehabilitation training was explored. The effects of the three current rehabilitation treatment methods on the microenvironment in a microscopic nonlinear model were innovatively unified and a complex system mapping relationship from the microscopic axon growth to macroscopic motor functions was constructed. The basic structure of the model was determined by simulating and fitting the data of the open rat experiments. A clinical rehabilitation experiment of spinal cord injury based on brain-computer interface was built, recruiting a patient with complete spinal cord injury, and the rehabilitation training and follow-up were conducted. The changes in the motor function of the patient was simulated and predicted through the constructed model, and the trend in the motor function improvement was successfully predicted over time. This proposed model explores the mechanism of brain-computer interface in rehabilitating patients with complete spinal cord injury, and it is also an application of complex system theory in rehabilitation medicine.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09804-3.}, }
@article {pmid36704007, year = {2022}, author = {de Oliveira, IH and Rodrigues, AC}, title = {Empirical comparison of deep learning methods for EEG decoding.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1003984}, pmid = {36704007}, issn = {1662-4548}, abstract = {Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches. Therefore, it is believed that these techniques can also improve the process of decoding brain signals in BMI systems. In this work, we present the implementation of two deep learning-based decoders and we compared the results with other state of art deep learning methods. The first decoder uses long short-term memory (LSTM) recurrent neural network and the second, entitled EEGNet-LSTM, combines a well-known neural decoder based on convolutional neural networks, called EEGNet, with some LSTM layers. The decoders have been tested using data set 2a from BCI Competition IV, and the results showed that the EEGNet-LSTM decoder has been approximately 23% better than the competition-winning decoder. A Wilcoxon t-test showed a significant difference between the two decoders (Z = 2.524, p = 0.012). The LSTM-based decoder has been approximately 9% higher than the best decoder from the same competition. However, there was no significant difference (Z = 1.540, p = 0.123). In order to verify the replication of the EEGNet-LSTM decoder on another data, we performed a test with PhysioNet's Physiobank EEG Motor Movement/Imagery dataset. The EEGNet-LSTM presented a higher performance (0.85 accuracy) than the EEGNet (0.82 accuracy). The results of this work can be important for the development of new research, as well as EEG-based BMI systems, which can benefit from the high precision of neural decoders.}, }
@article {pmid36699986, year = {2023}, author = {Shang, Q and Ma, H and Wang, C and Gao, L}, title = {Effects of Background Fitting of e-Commerce Live Streaming on Consumers' Purchase Intentions: A Cognitive-Affective Perspective.}, journal = {Psychology research and behavior management}, volume = {16}, number = {}, pages = {149-168}, pmid = {36699986}, issn = {1179-1578}, abstract = {PURPOSE: The purpose of this paper is to explore the effects of the background fitting of e-commerce live streaming on consumers' purchase intentions and the relevant internal psychological mechanism from the cognitive-affective perspective.<br><br>METHODS: In this study, a theoretical framework model of SOR comprising six variables is established. SPSS and SmartPLS are used to test the model and analyze data collected from a comprehensive questionnaire survey of 424 Chinese online consumers.<br><br>RESULTS: Results demonstrate that the impact of background fitting in e-commerce live streaming on consumers' purchase intentions can be divided into three stages. In the first stage, background fitting (comprised of both product-background fit and anchor-background fit) positively affect consumer cognitive process (perceived trust and perceived value). Perceived trust is mainly affected by anchor-background fit, while perceived value is mainly affected by product-background fit. In the second stage, consumers' cognitive process subsequently affects their affective process (perceived pleasure). Perceived value also has a greater positive effect on consumers' perceived pleasure than perceived trust, although perceived trust is a prerequisite for improving perceived value. In the third stage, the affective process further promotes consumers' purchase intentions.<br><br>CONCLUSION: Combining both SOR theory and cognitive-affective perspective, this study reveals that the internal influence mechanism of background fitting in e-commerce live streaming on consumers' purchase intentions is divided into three stages. Theoretically, this study not only expands the application of SOR theory in the research field of e-commerce live streaming from the perspective of external background stimulation, but also importantly contributes to the application of cognitive-emotional perspective in e-commerce live streaming. Practically, the study suggests optimizing background fitting as an effective way to improve consumer purchase intention in e-commerce live streaming, and it is better to optimize background fitting from the perspective of improving perceived trust, perceived value, and perceived pleasure.}, }
@article {pmid36699541, year = {2022}, author = {Hu, J and Wang, Y and Tong, Y and Lin, G and Li, Y and Chen, J and Xu, D and Wang, L and Bai, R}, title = {Thalamic structure and anastomosis in different hemispheres of moyamoya disease.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1058137}, pmid = {36699541}, issn = {1662-4548}, abstract = {OBJECTIVE: The progression of the asymptomatic hemisphere of moyamoya disease (MMD) is largely unknown. In this study, we investigated the differences in subcortical gray matter structure and angiographic features between asymptomatic and symptomatic hemispheres in patients with MMD.<br><br>METHODS: We retrospectively reviewed patients with MMD in consecutive cases in our center. We compared subcortical gray matter volume and three types of collaterals (lenticulostriate anastomosis, thalamic anastomosis, and choroidal anastomosis) between symptomatic and asymptomatic hemispheres. Symptomatic hemispheres were classified as ischemic hemisphere (i-hemisphere) and hemorrhagic hemisphere (h-hemisphere). Asymptomatic hemispheres were classified as contralateral asymptomatic hemisphere of i-hemisphere (ai-hemisphere), contralateral asymptomatic hemisphere of h-hemisphere (ah-hemisphere), bilateral asymptomatic hemispheres in asymptomatic group (aa-hemisphere).<br><br>RESULTS: A total of 117 MMD patients were reviewed, and 49 of them met the inclusion criteria, with 98 hemispheres being analyzed. The thalamic volume was found to differ significantly between the i- and ai-hemispheres (P = 0.010), between the i- and ah-hemispheres (P = 0.004), as well as between the h- and ai-hemispheres (P = 0.002), between the h- and ah-hemispheres (P < 0.001). There was a higher incidence of thalamic anastomosis in the ai-hemispheres than i-hemispheres (31.3% vs. 6.3%, P = 0.070), and in the ah-hemispheres than h-hemispheres (29.6% vs. 11.1%, P = 0.088). Additionally, the hemispheres with thalamic anastomosis had a significantly greater volume than those without thalamic anastomosis (P = 0.024). Univariate and multivariate logistic regression analysis showed that thalamic volume was closely associated with thalamic anastomosis.<br><br>CONCLUSION: The thalamic volume and the incidence of thalamic anastomosis increase in asymptomatic hemispheres and decrease in symptomatic hemispheres. Combining these two characteristics may be helpful in assessing the risk of stroke in the asymptomatic hemispheres of MMD as well as understanding the pathological evolution of the disease.}, }
@article {pmid36699533, year = {2022}, author = {Li, Y and Zhang, X and Ming, D}, title = {Early-stage fusion of EEG and fNIRS improves classification of motor imagery.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1062889}, pmid = {36699533}, issn = {1662-4548}, abstract = {INTRODUCTION: Many research papers have reported successful implementation of hybrid brain-computer interfaces by complementarily combining EEG and fNIRS, to improve classification performance. However, modality or feature fusion of EEG and fNIRS was usually designed for specific user cases, which were generally customized and hard to be generalized. How to effectively utilize information from the two modalities was still unclear.<br><br>METHODS: In this paper, we conducted a study to investigate the stage of bi-modal fusion based on EEG and fNIRS. A Y-shaped neural network was proposed and evaluated on an open dataset, which fuses the bimodal information in different stages.<br><br>RESULTS: The results suggests that the early-stage fusion of EEG and fNIRS have significantly higher performance compared to middle-stage and late-stage fusion network configuration (N = 57, P < 0.05). With the proposed framework, the average accuracy of 29 participants reaches 76.21% in the left-or-right hand motor imagery task in leave-one-out cross-validation, using bi-modal data as network inputs respectively, which is in the same level as the state-of-the-art hybrid BCI methods based on EEG and fNIRS data.}, }
@article {pmid36698872, year = {2022}, author = {Zanona, AF and Piscitelli, D and Seixas, VM and Scipioni, KRDDS and Bastos, MSC and de Sá, LCK and Monte-Silva, K and Bolivar, M and Solnik, S and De Souza, RF}, title = {Brain-computer interface combined with mental practice and occupational therapy enhances upper limb motor recovery, activities of daily living, and participation in subacute stroke.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1041978}, pmid = {36698872}, issn = {1664-2295}, abstract = {BACKGROUND: We investigated the effects of brain-computer interface (BCI) combined with mental practice (MP) and occupational therapy (OT) on performance in activities of daily living (ADL) in stroke survivors.<br><br>METHODS: Participants were randomized into two groups: experimental (n = 23, BCI controlling a hand exoskeleton combined with MP and OT) and control (n = 21, OT). Subjects were assessed with the functional independence measure (FIM), motor activity log (MAL), amount of use (MAL-AOM), and quality of movement (MAL-QOM). The box and blocks test (BBT) and the Jebsen hand functional test (JHFT) were used for the primary outcome of performance in ADL, while the Fugl-Meyer Assessment was used for the secondary outcome. Exoskeleton activation and the degree of motor imagery (measured as event-related desynchronization) were assessed in the experimental group. For the BCI, the EEG electrodes were placed on the regions of FC3, C3, CP3, FC4, C4, and CP4, according to the international 10-20 EEG system. The exoskeleton was placed on the affected hand. MP was based on functional tasks. OT consisted of ADL training, muscle mobilization, reaching tasks, manipulation and prehension, mirror therapy, and high-frequency therapeutic vibration. The protocol lasted 1 h, five times a week, for 2 weeks.<br><br>RESULTS: There was a difference between baseline and post-intervention analysis for the experimental group in all evaluations: FIM (p = 0.001, d = 0.56), MAL-AOM (p = 0.001, d = 0.83), MAL-QOM (p = 0.006, d = 0.84), BBT (p = 0.004, d = 0.40), and JHFT (p = 0.001, d = 0.45). Within the experimental group, post-intervention improvements were detected in the degree of motor imagery (p < 0.001) and the amount of exoskeleton activations (p < 0.001). For the control group, differences were detected for MAL-AOM (p = 0.001, d = 0.72), MAL-QOM (p = 0.013, d = 0.50), and BBT (p = 0.005, d = 0.23). Notably, the effect sizes were larger for the experimental group. No differences were detected between groups at post-intervention.<br><br>CONCLUSION: BCI combined with MP and OT is a promising tool for promoting sensorimotor recovery of the upper limb and functional independence in subacute post-stroke survivors.}, }
@article {pmid36698168, year = {2023}, author = {Lim, CG and Soh, CP and Lim, SSY and Fung, DSS and Guan, C and Lee, TS}, title = {Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial.}, journal = {Child and adolescent psychiatry and mental health}, volume = {17}, number = {1}, pages = {15}, pmid = {36698168}, issn = {1753-2000}, abstract = {BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a prevalent child neurodevelopmental disorder that is treated in clinics and in schools. Previous trials suggested that our brain-computer interface (BCI)-based attention training program could improve ADHD symptoms. We have since developed a tablet version of the training program which can be paired with wireless EEG headsets. In this trial, we investigated the feasibility of delivering this tablet-based BCI intervention at home.<br><br>METHODS: Twenty children diagnosed with ADHD, who did not receive any medication for the preceding month, were randomised to receive the 8-week tablet-based BCI intervention either in the clinic or at home. Those in the home intervention group received instructions before commencing the program and got reminders if they were lagging on the training sessions. The ADHD Rating Scale was completed by a blinded clinician at baseline and at week 8. Adverse events were monitored during any contact with the child throughout the trial and at week 8.<br><br>RESULTS: Children in both groups could complete the tablet-based intervention easily on their own with minimal support from the clinic therapist or their parents (at home). The intervention was safe with few reported adverse effects. Clinician-rated inattentive symptoms on the ADHD-Rating Scale reduced by 3.2 (SD 6.20) and 3.9 (SD 5.08) for the home-based and clinic-based groups respectively, suggesting that home-based intervention was comparable to clinic-based intervention.<br><br>CONCLUSIONS: This trial demonstrated that the tablet version of our BCI-based attention training program can be safely delivered to children in the comfort of their own home. Trial registration This trial is registered at clinicaltrials.gov as NCT01344044.}, }
@article {pmid36696073, year = {2023}, author = {Öztürk, S and Devecioğlu, &#x130; and Güçlü, B}, title = {Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex.}, journal = {Journal of computational neuroscience}, volume = {}, number = {}, pages = {}, pmid = {36696073}, issn = {1573-6873}, abstract = {Decoding of sensorimotor information is essential for brain-computer interfaces (BCIs) as well as in normal functioning organisms. In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 µm, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), overlapping also with the associated representation in the primary motor cortex (M1). Single-/multi-unit average spike rate (Rd) within the stimulus analysis window was used as the predictor of the stimulus state and the behavioral response at each trial based on a Bayesian network model. Due to high neural and psychophysical response variability for each rat and also across subjects, mean Rd was not correlated with hit and false alarm rates. Despite the fluctuations in the neural data, the Bayesian model for each rat generated moderately good accuracy (0.60-0.90) and good class prediction scores (recall, precision, F1) and was also tested with subsets of data (e.g. regular vs. fast spike groups). It was generally observed that the models were better for rats with lower psychophysical performance (lower sensitivity index A'). This suggests that Bayesian inference and similar machine learning techniques may be especially helpful during the training phase of BCIs or for rehabilitation with neuroprostheses.}, }
@article {pmid36693374, year = {2023}, author = {Fan, Z and Chang, J and Liang, Y and Zhu, H and Zhang, C and Zheng, D and Wang, J and Xu, Y and Li, QJ and Hu, H}, title = {Neural mechanism underlying depressive-like state associated with social status loss.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2022.12.033}, pmid = {36693374}, issn = {1097-4172}, abstract = {Downward social mobility is a well-known mental risk factor for depression, but its neural mechanism remains elusive. Here, by forcing mice to lose against their subordinates in a non-violent social contest, we lower their social ranks stably and induce depressive-like behaviors. These rank-decline-associated depressive-like behaviors can be reversed by regaining social status. In vivo fiber photometry and single-unit electrophysiological recording show that forced loss, but not natural loss, generates negative reward prediction error (RPE). Through the lateral hypothalamus, the RPE strongly activates the brain's anti-reward center, the lateral habenula (LHb). LHb activation inhibits the medial prefrontal cortex (mPFC) that controls social competitiveness and reinforces retreats in contests. These results reveal the core neural mechanisms mutually promoting social status loss and depressive behaviors. The intertwined neuronal signaling controlling mPFC and LHb activities provides a mechanistic foundation for the crosstalk between social mobility and psychological disorder, unveiling a promising target for intervention.}, }
@article {pmid36693292, year = {2023}, author = {Santamaría-Vázquez, E and Martínez-Cagigal, V and Marcos-Martínez, D and Rodríguez-González, V and Pérez-Velasco, S and Moreno-Calderón, S and Hornero, R}, title = {MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research.}, journal = {Computer methods and programs in biomedicine}, volume = {230}, number = {}, pages = {107357}, doi = {10.1016/j.cmpb.2023.107357}, pmid = {36693292}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations.<br><br>METHODS: We followed strict development practices to optimize MEDUSA&#xa9; for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages.<br><br>RESULTS: MEDUSA&#xa9; provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility.<br><br>CONCLUSIONS: MEDUSA&#xa9; is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.}, }
@article {pmid36693278, year = {2023}, author = {Johnston, R and Abbass, M and Corrigan, B and Martinez-Trujillo, J and Sachs, A}, title = {Decoding spatial locations from primate lateral prefrontal cortex neural activity during virtual navigation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb5c2}, pmid = {36693278}, issn = {1741-2552}, abstract = {OBJECTIVE: Decoding the intended trajectories from brain signals using a brain-computer interface system could be used to improve the mobility of patients with disabilities.<br><br>APPROACH: Neuronal activity associated with spatial locations was examined while macaques performed a navigation task within a virtual environment.<br><br>MAIN RESULTS: Here, we provide proof of principle that multi-unit spiking activity recorded from the lateral prefrontal cortex of non-human primates can be used to predict the location of a subject in a virtual maze during a navigation task. The spatial positions within the maze that require a choice or are associated with relevant task events can be better predicted than the locations where no relevant events occur. Importantly, within a task epoch of a single trial, multiple locations along the maze can be independently identified using a support vector machine model.<br><br>SIGNIFICANCE: Considering that the lateral prefrontal cortex of macaques and humans share similar properties, our results suggest that this area could be a valuable implant location for an intracortical brain computer interface system used for spatial navigation in patients with disabilities.}, }
@article {pmid36689427, year = {2023}, author = {Pattisapu, S and Ray, S}, title = {Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier.}, journal = {PloS one}, volume = {18}, number = {1}, pages = {e0279881}, doi = {10.1371/journal.pone.0279881}, pmid = {36689427}, issn = {1932-6203}, abstract = {Stimulus-induced narrow-band gamma oscillations (30-70 Hz) in human electro-encephalograph (EEG) have been linked to attentional and memory mechanisms and are abnormal in mental health conditions such as autism, schizophrenia and Alzheimer's Disease. However, since the absolute power in EEG decreases rapidly with increasing frequency following a "1/f" power law, and the gamma band includes line noise frequency, these oscillations are highly susceptible to instrument noise. Previous studies that recorded stimulus-induced gamma oscillations used expensive research-grade EEG amplifiers to address this issue. While low-cost EEG amplifiers have become popular in Brain Computer Interface applications that mainly rely on low-frequency oscillations (< 30 Hz) or steady-state-visually-evoked-potentials, whether they can also be used to measure stimulus-induced gamma oscillations is unknown. We recorded EEG signals using a low-cost, open-source amplifier (OpenBCI) and a traditional, research-grade amplifier (Brain Products GmbH), both connected to the OpenBCI cap, in male (N = 6) and female (N = 5) subjects (22-29 years) while they viewed full-screen static gratings that are known to induce two distinct gamma oscillations: slow and fast gamma, in a subset of subjects. While the EEG signals from OpenBCI were considerably noisier, we found that out of the seven subjects who showed a gamma response in Brain Products recordings, six showed a gamma response in OpenBCI as well. In spite of the noise in the OpenBCI setup, the spectral and temporal profiles of these responses in alpha (8-13 Hz) and gamma bands were highly correlated between OpenBCI and Brain Products recordings. These results suggest that low-cost amplifiers can potentially be used in stimulus-induced gamma response detection.}, }
@article {pmid36683147, year = {2023}, author = {Jin, J and Xu, Z and Zhang, L and Zhang, C and Zhao, X and Mao, Y and Zhang, H and Liang, X and Wu, J and Yang, Y and Zhang, J}, title = {Gut-derived β-amyloid: Likely a centerpiece of the gut-brain axis contributing to Alzheimer's pathogenesis.}, journal = {Gut microbes}, volume = {15}, number = {1}, pages = {2167172}, doi = {10.1080/19490976.2023.2167172}, pmid = {36683147}, issn = {1949-0984}, abstract = {Peripheral β-amyloid (Aβ), including those contained in the gut, may contribute to the formation of Aβ plaques in the brain, and gut microbiota appears to exert an impact on Alzheimer's disease (AD) via the gut-brain axis, although detailed mechanisms are not clearly defined. The current study focused on uncovering the potential interactions among gut-derived Aβ in aging, gut microbiota, and AD pathogenesis. To achieve this goal, the expression levels of Aβ and several key proteins involved in Aβ metabolism were initially assessed in mouse gut, with key results confirmed in human tissue. The results demonstrated that a high level of Aβ was detected throughout the gut in both mice and human, and gut Aβ42 increased with age in wild type and mutant amyloid precursor protein/presenilin 1 (APP/PS1) mice. Next, the gut microbiome of mice was characterized by 16S rRNA sequencing, and we found the gut microbiome altered significantly in aged APP/PS1 mice and fecal microbiota transplantation (FMT) of aged APP/PS1 mice increased gut BACE1 and Aβ42 levels. Intra-intestinal injection of isotope or fluorescence labeled Aβ combined with vagotomy was also performed to investigate the transmission of Aβ from gut to brain. The data showed that, in aged mice, the gut Aβ42 was transported to the brain mainly via blood rather than the vagal nerve. Furthermore, FMT of APP/PS1 mice induced neuroinflammation, a phenotype that mimics early AD pathology. Taken together, this study suggests that the gut is likely a critical source of Aβ in the brain, and gut microbiota can further upregulate gut Aβ production, thereby potentially contributing to AD pathogenesis.}, }
@article {pmid36682180, year = {2023}, author = {Peng, G and Zhao, K and Zhang, H and Xu, D and Kong, X}, title = {Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis.}, journal = {Computers in biology and medicine}, volume = {154}, number = {}, pages = {106537}, doi = {10.1016/j.compbiomed.2023.106537}, pmid = {36682180}, issn = {1879-0534}, abstract = {Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-computer fusion. EEG signals have inherent temporal and spatial characteristics. However, existing studies did not fully consider the two properties. In addition, the position encoding mechanism in the vanilla transformer cannot effectively encode the continuous temporal character of the emotion. A temporal relative (TR) encoding mechanism is proposed to encode the temporal EEG signals for constructing the temporality self-attention in the transformer. To explore the contribution of each EEG channel corresponding to the electrode on the cerebral cortex to emotion analysis, a channel-attention (CA) mechanism is presented. The temporality self-attention mechanism cooperates with the channel-attention mechanism to utilize the temporal and spatial information of EEG signals simultaneously by preprocessing. Exhaustive experiments are conducted on the DEAP dataset, including the binary classification on valence, arousal, dominance, and liking. Furthermore, the discrete emotion category classification task is also conducted by mapping the dimensional annotations of DEAP into discrete emotion categories (5-class). Experimental results demonstrate that our model outperforms the advanced methods for all classification tasks.}, }
@article {pmid36682005, year = {2023}, author = {Guo, B and Zheng, H and Jiang, H and Li, X and Guan, N and Zuo, Y and Zhang, Y and Yang, H and Wang, X}, title = {Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.}, journal = {Briefings in bioinformatics}, volume = {}, number = {}, pages = {}, doi = {10.1093/bib/bbac628}, pmid = {36682005}, issn = {1477-4054}, abstract = {Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.}, }
@article {pmid36680589, year = {2023}, author = {Afreen, A and Ahmed, Z and Khalid, N and Ferheen, I and Ahmed, I}, title = {Optimization and cholesterol-lowering activity of exopolysaccharide from Lactiplantibacillus paraplantarum NCCP 962.}, journal = {Applied microbiology and biotechnology}, volume = {}, number = {}, pages = {}, pmid = {36680589}, issn = {1432-0614}, abstract = {Exopolysaccharides (EPSs) are biological polymers with unique structural features have gained particular interest in the fields of food, chemistry and medicine, and food industry. EPS from the food-grade lactic acid bacteria (LAB) can be used as a natural food additives to commercial ones in the processing and development of functional foods and nutraceuticals. The current study was aimed to explore the EPS-producing LAB from the dahi; to optimize the fermentation conditions through Plackett-Burman (PB) and response surface methodology (RSM); and to study its physicochemical, rheological, functional attributes, and cholesterol-lowering activity. Lactiplantibacillus paraplantarum NCCP 962 was isolated among the 08 strains screened at the initial stage. The PB design screened out four independent factors that had a significant positive effect, i.e., lactose, yeast extract, CaCl2, and tryptone, while the remaining seven had a non-significant effect. The RSM exhibited lactose, yeast extract, and CaCl2, significantly contributing to EPS yield. The maximum EPS yield (0.910 g/L) was obtained at 6.57% lactose, 0.047% yeast extract, 0.59% CaCl2, and 1.37% tryptone. The R[2] value above 97% explains the higher variability and depicts the model's validity. The resulted EPS was a heteropolysaccharide in nature with mannose, glucose, and galactose monosaccharides. FTIR spectrum reflected the presence of functional groups, i.e., O-H, C-H, C = O, C-O-H, and CH2. SEM revealed a porous and rough morphology of EPS, also found to be thermally stable and negligible weight loss, i.e., 14.0% at 257 °C and 35.4% at 292.9 °C was observed in the 1st and 2nd phases, respectively. Rheological attributes revealed that strain NCCP 962 had high viscosity by increasing the EPS concentration, low pH, and temperature with respectable water holding, oil capacities, foaming abilities, and stability. NCCP 962 EPS possessed up to 46.4% reduction in cholesterol concentration in the supernatant. Conclusively, these results suggested that strain NCCP 962 can be used in food processing applications and other medical fields. KEY POINTS: • The fermentation conditions affect EPS yield from L. paraplantarum and significantly increased yield to 0.910 g/L. • The EPS was heteropolysaccharide in nature and thermally stable with amorphous morphology. • Good cholesterol-lowering potential with the best rheological, emulsifying, and foaming capacities.}, }
@article {pmid36679557, year = {2023}, author = {Lupenko, S and Butsiy, R and Shakhovska, N}, title = {Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {2}, pages = {}, doi = {10.3390/s23020760}, pmid = {36679557}, issn = {1424-8220}, abstract = {In this study is substantiated the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain-computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain-computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain-computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain-computer interface operator is experimentally established. The application of Bessel's inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain-computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain-computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.}, }
@article {pmid36679501, year = {2023}, author = {Milanés-Hermosilla, D and Trujillo-Codorniú, R and Lamar-Carbonell, S and Sagaró-Zamora, R and Tamayo-Pacheco, JJ and Villarejo-Mayor, JJ and Delisle-Rodriguez, D}, title = {Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {2}, pages = {}, doi = {10.3390/s23020703}, pmid = {36679501}, issn = {1424-8220}, abstract = {The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.}, }
@article {pmid36676348, year = {2023}, author = {Wen, J and Tang, L and Zhang, S and Zhan, Q and Wang, Y}, title = {Qualitative and Quantitative Investigations on the Failure Effect of Critical Fissures in Rock Specimens under Plane Strain Compression.}, journal = {Materials (Basel, Switzerland)}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/ma16020611}, pmid = {36676348}, issn = {1996-1944}, abstract = {To investigate the failure effects of critical fissures in rock specimens subjected to plane strain compression (PSC), five types of internal fissures in rock specimens were designed and twelve PSC tests were conducted for two lithologies based on the discrete element method (DEM). The results were analyzed in terms of the fracture mode, data characteristics, and crack evolution. The results indicated the following. (1) The rock samples with a critical fissure under PSC showed a weak face shear fracture mode, which was influenced by lithology, fissure angle, and fissure surface direction. (2) There were four critical expansion points (CEPs) of axial stress of the rocks under PSC, which were the stage signs of rock materials from local damage to complete fracture. The rock-bearing capacity index (RockBCI) was further proposed. (3) The bearing capacity of rock samples with horizontal fissures, fissures whose angles coincided with the fracture surface, and fissures whose surface was perpendicular to the lateral confine direction was the worst; their BCI[2] values were found to be 80.6%, 70.8%, and 56.9% of the rock samples without any fissures, respectively. The delayed fracture situation under PSC was identified and analyzed. (4) The crack evolution followed the unified law of localization, and the fissures in the rocks changed the mode of crack development and the path of the deepening and connecting of crack clusters, as well as affecting the time process from damage to collapse. This research innovatively investigated the behavior characteristics of rock samples with a fissure under PSC, and it qualitatively and quantitatively analyzed the bearing capacity of rock mass from local damage to fracture.}, }
@article {pmid36675707, year = {2022}, author = {Ma, Y and Gong, A and Nan, W and Ding, P and Wang, F and Fu, Y}, title = {Personalized Brain-Computer Interface and Its Applications.}, journal = {Journal of personalized medicine}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/jpm13010046}, pmid = {36675707}, issn = {2075-4426}, abstract = {Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.}, }
@article {pmid36675486, year = {2023}, author = {Morone, G and Pichiorri, F}, title = {Post-Stroke Rehabilitation: Challenges and New Perspectives.}, journal = {Journal of clinical medicine}, volume = {12}, number = {2}, pages = {}, doi = {10.3390/jcm12020550}, pmid = {36675486}, issn = {2077-0383}, abstract = {A stroke is determined by insufficient blood supply to the brain due to vessel occlusion (ischemic stroke) or rupture (hemorrhagic stroke), resulting in immediate neurological impairment to differing degrees [...].}, }
@article {pmid36672726, year = {2023}, author = {Coelho, HRS and Neves, SCD and Menezes, JNDS and Antoniolli-Silva, ACMB and Oliveira, RJ}, title = {Mesenchymal Stromal Cell Therapy Reverses Detrusor Hypoactivity in a Chronic Kidney Patient.}, journal = {Biomedicines}, volume = {11}, number = {1}, pages = {}, doi = {10.3390/biomedicines11010218}, pmid = {36672726}, issn = {2227-9059}, abstract = {Detrusor hypoactivity (DH) is characterized by low detrusor pressure or a short contraction associated with low urinary flow. This condition can progress to chronic renal failure (CRF) and result in the need for dialysis. The present case report demonstrates that a patient diagnosed with DH and CRF who received two transplants with 2 × 10[6] autologous mesenchymal stromal cells at an interval of 30 days recovered the contractile strength of the bladder and normalized his renal function. The patient had a score of 19 on the ICIQ-SF before cell therapy, and that score was reduced to 1 after transplantation. These results demonstrate that there was an improvement in his voiding function, urinary stream and urine volume as evaluated by urofluxometry. In addition, a urodynamic study carried out after treatment showed an increase in the maximum flow from 2 mL/s to 23 mL/s, the detrusor pressure in the maximum flow from 21 cm H2O to 46 cm H2O and a BCI that went from 31 to 161, characterizing good detrusor contraction. Thus, in the present case, the transplantation of autologous mesenchymal stromal cells proved to be a viable therapeutic option to allow the patient to recover the contractile strength of the bladder, and reversed the CRF.}, }
@article {pmid36672115, year = {2023}, author = {Zhao, ZP and Nie, C and Jiang, CT and Cao, SH and Tian, KX and Yu, S and Gu, JW}, title = {Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010134}, pmid = {36672115}, issn = {2076-3425}, abstract = {Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.}, }
@article {pmid36672052, year = {2022}, author = {Pepi, C and Mercier, M and Carfì Pavia, G and de Benedictis, A and Vigevano, F and Rossi-Espagnet, MC and Falcicchio, G and Marras, CE and Specchio, N and de Palma, L}, title = {Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010071}, pmid = {36672052}, issn = {2076-3425}, abstract = {OBJECTIVES: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome.<br><br>METHODS: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy.<br><br>RESULTS: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified.<br><br>CONCLUSIONS: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT.<br><br>SIGNIFICANCE: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.}, }
@article {pmid36672050, year = {2022}, author = {Gao, T and Hu, Y and Zhuang, J and Bai, Y and Lu, R}, title = {Repetitive Transcranial Magnetic Stimulation of the Brain Region Activated by Motor Imagery Involving a Paretic Wrist and Hand for Upper-Extremity Motor Improvement in Severe Stroke: A Preliminary Study.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010069}, pmid = {36672050}, issn = {2076-3425}, abstract = {Approximately two-thirds of stroke survivors experience chronic upper-limb paresis; however, treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) can enhance motor function recovery in stroke survivors, but its efficacy is controversial. We compared the efficacy of stimulating different targets in 10 chronic stroke patients with severe upper-limb motor impairment. Motor imagery-based brain-computer interface training augmented with virtual reality was used to induce neural activity in the brain region during an imagery task. Participants were then randomly assigned to two groups: an experimental group (received high-frequency rTMS delivered to the brain region activated earlier) and a comparison group (received low-frequency rTMS delivered to the contralesional primary motor cortex). Behavioural metrics and diffusion tensor imaging were compared pre- and post rTMS. After the intervention, participants in both groups improved somewhat. This preliminary study indicates that in chronic stroke patients with severe upper-limb motor impairment, inducing activation in specific brain regions during motor imagery tasks and selecting these regions as a target is feasible. Further studies are needed to explore the efficacy of this intervention.}, }
@article {pmid36672046, year = {2022}, author = {Adama, S and Bogdan, M}, title = {Application of Soft-Clustering to Assess Consciousness in a CLIS Patient.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010065}, pmid = {36672046}, issn = {2076-3425}, abstract = {Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.}, }
@article {pmid36672038, year = {2022}, author = {Fu, J and Chen, S and Jia, J}, title = {Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010056}, pmid = {36672038}, issn = {2076-3425}, abstract = {Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.}, }
@article {pmid36672034, year = {2022}, author = {Gao, X and Yang, Y and Zhang, F and Zhou, F and Zhu, J and Sun, J and Xu, K and Chen, Y}, title = {A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010052}, pmid = {36672034}, issn = {2076-3425}, abstract = {Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.}, }
@article {pmid36671894, year = {2022}, author = {Xu, M and Zhao, Y and Xu, G and Zhang, Y and Sun, S and Sun, Y and Wang, J and Pei, R}, title = {Recent Development of Neural Microelectrodes with Dual-Mode Detection.}, journal = {Biosensors}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/bios13010059}, pmid = {36671894}, issn = {2079-6374}, abstract = {Neurons communicate through complex chemical and electrophysiological signal patterns to develop a tight information network. A physiological or pathological event cannot be explained by signal communication mode. Therefore, dual-mode electrodes can simultaneously monitor the chemical and electrophysiological signals in the brain. They have been invented as an essential tool for brain science research and brain-computer interface (BCI) to obtain more important information and capture the characteristics of the neural network. Electrochemical sensors are the most popular methods for monitoring neurochemical levels in vivo. They are combined with neural microelectrodes to record neural electrical activity. They simultaneously detect the neurochemical and electrical activity of neurons in vivo using high spatial and temporal resolutions. This paper systematically reviews the latest development of neural microelectrodes depending on electrode materials for simultaneous in vivo electrochemical sensing and electrophysiological signal recording. This includes carbon-based microelectrodes, silicon-based microelectrode arrays (MEAs), and ceramic-based MEAs, focusing on the latest progress since 2018. In addition, the structure and interface design of various types of neural microelectrodes have been comprehensively described and compared. This could be the key to simultaneously detecting electrochemical and electrophysiological signals.}, }
@article {pmid36669202, year = {2023}, author = {Ming, G and Pei, W and Gao, X and Wang, Y}, title = {A high-performance SSVEP-based BCI using imperceptible flickers.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb50e}, pmid = {36669202}, issn = {1741-2552}, abstract = {Objective.Existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience and system performance. This study proposed an individualized space and phase modulation method to code imperceptible flickers at 60 Hz towards a user-friendly SSVEP-based BCI with high performance.Approach.The individualized customization of visual stimulation took the subject-to-subject variability in cortex geometry into account. An annulus global-stimulation was divided into local-stimulations of eight annular sectors and presented to subjects separately. The local-stimulation SSVEPs were superimposed to simulate global-stimulation SSVEPs with 4[7]space and phase coding combinations. A four-class phase-coded BCI diagram was used to evaluate the simulated classification performance. The performance ranking of all simulated global-stimulation SSVEPs were obtained and three performance levels (optimal, medium, worst) of individualized modulation groups were searched for each subject. The standard-modulation group conforming to the V1 'cruciform' geometry and the non-modulation group were involved as controls. A four-target phase-coded BCI system with SSVEPs at 60 Hz was implemented with the five modulation groups and questionnaires were used to evaluate user experience.Main results.The proposed individualized space and phase modulation method effectively modulated the SSVEP intensity without affecting the user experience. The online BCI system using the 60 Hz stimuli achieved mean information transfer rates of 52.8 ± 1.9 bits min[-1], 16.8 ± 2.4 bits min[-1], and 42.4 ± 3.0 bits min[-1]with individualized optimal-modulation, individualized worst-modulation, and non-modulation groups, respectively.Significance.Structural and functional characteristics of the human visual cortex were exploited to enhance the response intensity of SSVEPs at 60 Hz, resulting in a high-performance BCI system with good user experience. This study has important theoretical significance and application value for promoting the development of the visual BCI technology.}, }
@article {pmid36662378, year = {2023}, author = {Li, J and Wang, J and Wang, T and Kong, W and Xi, X}, title = {Quantification of body ownership awareness induced by the visual movement illusion of the lower limbs: a study of electroencephalogram and surface electromyography.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36662378}, issn = {1741-0444}, abstract = {The visual movement illusion (VMI) is a subjective experience. This illusion is produced by watching the subject's motion video. At the same time, VMI evokes awareness of body ownership. We applied the power spectral density (PSD) matrix and the partial directed correlation (PDC) matrix to build the PPDC matrix for the γ2 band (34-98.5 Hz), combining cerebral cortical and musculomotor cortical complexity and PPDC to quantify the degree of body ownership. Thirty-five healthy subjects were recruited to participate in this experiment. The subjects' electroencephalography (EEG) and surface electromyography (sEMG) data were recorded under resting conditions, observation conditions, illusion conditions, and actual seated front-kick movements. The results show the following: (1) VMI activates the cerebral cortex to some extent; (2) VMI enhances cortical muscle excitability in the rectus femoris and medial vastus muscles; (3) VMI induces a sense of body ownership; (4) the use of PPDC values, fuzzy entropy values of muscles, and fuzzy entropy values of the cerebral cortex can quantify whether VMI induces awareness of body ownership. These results illustrate that PPDC can be used as a biomarker to show that VMI affects changes in the cerebral cortex and as a quantitative tool to show whether body ownership awareness arises.}, }
@article {pmid36662082, year = {2023}, author = {Karbalaei Akbari, M and Siraj Lopa, N and Shahriari, M and Najafzadehkhoee, A and Galusek, D and Zhuiykov, S}, title = {Functional Two-Dimensional Materials for Bioelectronic Neural Interfacing.}, journal = {Journal of functional biomaterials}, volume = {14}, number = {1}, pages = {}, doi = {10.3390/jfb14010035}, pmid = {36662082}, issn = {2079-4983}, abstract = {Realizing the neurological information processing by analyzing the complex data transferring behavior of populations and individual neurons is one of the fast-growing fields of neuroscience and bioelectronic technologies. This field is anticipated to cover a wide range of advanced applications, including neural dynamic monitoring, understanding the neurological disorders, human brain-machine communications and even ambitious mind-controlled prosthetic implant systems. To fulfill the requirements of high spatial and temporal resolution recording of neural activities, electrical, optical and biosensing technologies are combined to develop multifunctional bioelectronic and neuro-signal probes. Advanced two-dimensional (2D) layered materials such as graphene, graphene oxide, transition metal dichalcogenides and MXenes with their atomic-layer thickness and multifunctional capabilities show bio-stimulation and multiple sensing properties. These characteristics are beneficial factors for development of ultrathin-film electrodes for flexible neural interfacing with minimum invasive chronic interfaces to the brain cells and cortex. The combination of incredible properties of 2D nanostructure places them in a unique position, as the main materials of choice, for multifunctional reception of neural activities. The current review highlights the recent achievements in 2D-based bioelectronic systems for monitoring of biophysiological indicators and biosignals at neural interfaces.}, }
@article {pmid36658415, year = {2023}, author = {Gu, J and Jiang, J and Ge, S and Wang, H}, title = {Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36658415}, issn = {1741-0444}, abstract = {The common spatial patterns (CSP) technique is an effective strategy for the classification of multichannel electroencephalogram (EEG) signals. However, the objective function expression of the conventional CSP algorithm is based on the L2-norm, which makes the performance of the method easily affected by outliers and noise. In this paper, we consider a new extension to CSP, which is termed capped L21-norm-based common spatial patterns (CCSP-L21), by using the capped L21-norm rather than the L2-norm for robust modeling. L21-norm considers the L1-norm sum which largely alleviates the influence of outliers and noise for the sake of robustness. The capped norm is further used to mitigate the effects of extreme outliers whose signal amplitude is much higher than that of the normal signal. Moreover, a non-greedy iterative procedure is derived to solve the proposed objective function. The experimental results show that the proposed method achieves the highest average recognition rates on the three real data sets of BCI competitions, which are 91.67%, 85.07%, and 82.04%, respectively. Capped L21-norm-based common spatial patterns-a robust model for EEG signals classification.}, }
@article {pmid36657633, year = {2023}, author = {Perez-Garcia, G and Bicak, M and Haure-Mirande, JV and Perez, GM and Otero-Pagan, A and Gama Sosa, MA and De Gasperi, R and Sano, M and Barlow, C and Gage, FH and Readhead, B and Ehrlich, ME and Gandy, S and Elder, GA}, title = {BCI-838, an orally active mGluR2/3 receptor antagonist pro-drug, rescues learning behavior deficits in the PS19 MAPT[P301S] mouse model of tauopathy.}, journal = {Neuroscience letters}, volume = {}, number = {}, pages = {137080}, doi = {10.1016/j.neulet.2023.137080}, pmid = {36657633}, issn = {1872-7972}, abstract = {Tauopathies are a heterogeneous group of neurodegenerative disorders that are clinically and pathologically distinct from Alzheimer's disease (AD) having tau inclusions in neurons and/or glia as their most prominent neuropathological feature. BCI-838 (MGS00210) is a group II metabotropic glutamate receptor (mGluR2/3) antagonist pro-drug. Previously, we reported that orally administered BCI-838 improved learning behavior and reduced anxiety in Dutch (APP[E693Q]) transgenic mice, a model of the pathological accumulation of Aβ oligomers found in AD. Herein, we investigated effects of BCI-838 on PS19 male mice that express the tauopathy mutation MAPT[P301S] associated with human frontotemporal lobar degeneration (FTLD). These mice develop an aging-related tauopathy without amyloid accumulation. Mice were divided into three experimental groups: (1) non-transgenic wild type mice treated with vehicle, (2) PS19 mice treated with vehicle and (3) PS19 mice treated with 5 mg/kg BCI-838. Groups of 10-13 mice were utilized. Vehicle or BCI-838 was administered by oral gavage for 4 weeks. Behavioral testing consisting of a novel object recognition task was conducted after drug administration. Two studies were performed beginning treatment of mice at 3 or 7 months of age. One month of BCI-838 treatment rescued deficits in recognition memory in PS19 mice whether treatment was begun at 3 or 7 months of age. These studies extend the potential utility of BCI-838 to neurodegenerative conditions that have tauopathy as their underlying basis. They also suggest an mGluR2/3 dependent mechanism as a basis for the behavioral deficits in PS19 mice.}, }
@article {pmid36657242, year = {2023}, author = {Proverbio, AM and Tacchini, M and Jiang, K}, title = {What do you have in mind? ERP markers of visual and auditory imagery.}, journal = {Brain and cognition}, volume = {166}, number = {}, pages = {105954}, doi = {10.1016/j.bandc.2023.105954}, pmid = {36657242}, issn = {1090-2147}, abstract = {This study aimed to investigate the psychophysiological markers of imagery processes through EEG/ERP recordings. Visual and auditory stimuli representing 10 different semantic categories were shown to 30 healthy participants. After a given interval and prompted by a light signal, participants were asked to activate a mental image corresponding to the semantic category for recording synchronized electrical potentials. Unprecedented electrophysiological markers of imagination were recorded in the absence of sensory stimulation. The following peaks were identified at specific scalp sites and latencies, during imagination of infants (centroparietal positivity, CPP, and late CPP), human faces (anterior negativity, AN), animals (anterior positivity, AP), music (P300-like), speech (N400-like), affective vocalizations (P2-like) and sensory (visual vs auditory) modality (PN300). Overall, perception and imagery conditions shared some common electro/cortical markers, but during imagery the category-dependent modulation of ERPs was long latency and more anterior, with respect to the perceptual condition. These ERP markers might be precious tools for BCI systems (pattern recognition, classification, or A.I. algorithms) applied to patients affected by consciousness disorders (e.g., in a vegetative or comatose state) or locked-in-patients (e.g., spinal or SLA patients).}, }
@article {pmid36656873, year = {2023}, author = {Pang, J and Peng, S and Hou, C and Zhao, H and Fan, Y and Ye, C and Zhang, N and Wang, T and Cao, Y and Zhou, W and Sun, D and Wang, K and Rümmeli, MH and Liu, H and Cuniberti, G}, title = {Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles.}, journal = {ACS sensors}, volume = {}, number = {}, pages = {}, doi = {10.1021/acssensors.2c02790}, pmid = {36656873}, issn = {2379-3694}, abstract = {Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain-machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.}, }
@article {pmid36655886, year = {2022}, author = {Breen, JR and Pensini, P}, title = {Grounded by Mother Nature's Revenge.}, journal = {Experimental psychology}, volume = {69}, number = {5}, pages = {284-294}, doi = {10.1027/1618-3169/a000566}, pmid = {36655886}, issn = {2190-5142}, abstract = {Leisure air travel is a popular form of tourism, but its emissions are a major contributor to anthropogenic climate change. Restrictions to leisure air travel have previously received little support; however, the same restrictions to mitigate the spread of COVID-19 have been popular. This support is unlikely to persist in a postpandemic world, highlighting the need for alternative ways to improve support for reducing leisure air travel. Anthropomorphism of nature has consistently predicted proenvironmental behavior, which has been mediated by guilt felt for harm to the environment. This research is the first empirical study to explore this relationship in the context of COVID-19, where it examined support for restricting leisure air travel to help mitigate (1) COVID-19 and (2) climate change. In an experimental online study, Australian residents (N = 325, Mage = 54.48, SDage = 14.63, 62% women) were recruited through social media. Anthropomorphism of nature in the context of COVID-19 (AMP-19) was manipulated through exposure to a news article. Participants then completed measures of environmental guilt and support for restricting leisure air travel to mitigate COVID-19 (LAT-19) and to mitigate climate change (LAT-CC). A significant indirect effect was observed in both models, such that AMP-19 predicted environmental guilt which in turn predicted LAT-19 (f[2] = .26; BCI [0.66, 3.87]) and LAT-CC (f[2] = .45; BCI [0.84, 5.06]). The results imply that anthropomorphism of nature in the context of COVID-19 can improve attitudes toward this proenvironmental behavior, with greater support when this was to mitigate climate change. Implications are discussed.}, }
@article {pmid36654858, year = {2023}, author = {Jin, S and Chen, X and Zheng, H and Cai, W and Lin, X and Kong, X and Ni, Y and Ye, J and Li, X and Shen, L and Guo, B and Abdelrahman, Z and Zhou, S and Mao, S and Wang, Y and Yao, C and Gu, X and Yu, B and Wang, Z and Wang, X}, title = {Downregulation of UBE4B promotes CNS axon regrowth and functional recovery after stroke.}, journal = {iScience}, volume = {26}, number = {1}, pages = {105885}, pmid = {36654858}, issn = {2589-0042}, abstract = {The limited intrinsic regrowth capacity of corticospinal axons impedes functional recovery after cortical stroke. Although the mammalian target of rapamycin (mTOR) and p53 pathways have been identified as the key intrinsic pathways regulating CNS axon regrowth, little is known about the key upstream regulatory mechanism by which these two major pathways control CNS axon regrowth. By screening genes that regulate ubiquitin-mediated degradation of the p53 proteins in mice, we found that ubiquitination factor E4B (UBE4B) represses axonal regrowth in retinal ganglion cells and corticospinal neurons. We found that axonal regrowth induced by UBE4B depletion depended on the cooperative activation of p53 and mTOR. Importantly, overexpression of UbV.E4B, a competitive inhibitor of UBE4B, in corticospinal neurons promoted corticospinal axon sprouting and facilitated the recovery of corticospinal axon-dependent function in a cortical stroke model. Thus, our findings provide a translatable strategy for restoring corticospinal tract-dependent functions after cortical stroke.}, }
@article {pmid36654371, year = {2021}, author = {Zhu, J and Chen, F and Luo, L and Wu, W and Dai, J and Zhong, J and Lin, X and Chai, C and Ding, P and Liang, L and Wang, S and Ding, X and Chen, Y and Wang, H and Qiu, J and Wang, F and Sun, C and Zeng, Y and Fang, J and Jiang, X and Liu, P and Tang, G and Qiu, X and Zhang, X and Ruan, Y and Jiang, S and Li, J and Zhu, S and Xu, X and Li, F and Liu, Z and Cao, G and Chen, D}, title = {Single-cell atlas of domestic pig cerebral cortex and hypothalamus.}, journal = {Science bulletin}, volume = {66}, number = {14}, pages = {1448-1461}, doi = {10.1016/j.scib.2021.04.002}, pmid = {36654371}, issn = {2095-9281}, abstract = {The brain of the domestic pig (Sus scrofa domesticus) has drawn considerable attention due to its high similarities to that of humans. However, the cellular compositions of the pig brain (PB) remain elusive. Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB (frontal lobe, parietal lobe, temporal lobe, occipital lobe, and hypothalamus) and identified 21 cell subpopulations. The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution. Furthermore, we identified cell types and molecular pathways closely associated with neurological disorders, bridging the gap between gene mutations and pathogenesis. We reported, to our knowledge, the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species, providing extensive resources for future research regarding neural science, evolutionary developmental biology, and regenerative medicine.}, }
@article {pmid36652620, year = {2023}, author = {Abbasi, J and Suran, M}, title = {From Thought to Text: How an Endovascular Brain-Computer Interface Could Help Patients With Severe Paralysis Communicate.}, journal = {JAMA}, volume = {}, number = {}, pages = {}, doi = {10.1001/jama.2022.24343}, pmid = {36652620}, issn = {1538-3598}, }
@article {pmid36652475, year = {2023}, author = {Liang, W and Balasubramanian, K and Papadourakis, V and Hatsopoulos, NG}, title = {Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {120}, number = {4}, pages = {e2212227120}, doi = {10.1073/pnas.2212227120}, pmid = {36652475}, issn = {1091-6490}, support = {NIH R01 NS111982/GF/NIH HHS/United States ; }, abstract = {Propagating spatiotemporal neural patterns are widely evident across sensory, motor, and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multielectrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200 to 400 Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatiotemporal characteristics in refining brain-machine interfaces.}, }
@article {pmid36607323, year = {2023}, author = {Zhang, Y and Schriver, KE and Hu, JM and Roe, AW}, title = {Spatial frequency representation in V2 and V4 of macaque monkey.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, doi = {10.7554/eLife.81794}, pmid = {36607323}, issn = {2050-084X}, abstract = {Spatial frequency (SF) is an important attribute in the visual scene and is a defining feature of visual processing channels. However, there remain many unsolved questions about how extrastriate areas in primate visual cortex code this fundamental information. Here, using intrinsic signal optical imaging in visual areas of V2 and V4 of macaque monkeys, we quantify the relationship between SF maps and (1) visual topography and (2) color and orientation maps. We find that in orientation regions, low to high SF is mapped orthogonally to orientation; in color regions, which are reported to contain orthogonal axes of color and lightness, low SFs tend to be represented more frequently than high SFs. This supports a population-based SF fluctuation related to the 'color/orientation' organizations. We propose a generalized hypercolumn model across cortical areas, comprised of two orthogonal parameters with additional parameters.}, }
@article {pmid36650644, year = {2023}, author = {Jiang, J and Fu, Y and Tang, A and Gao, X and Zhang, D and Shen, Y and Mou, T and Hu, S and Gao, J and Lai, J}, title = {Sex difference in prebiotics on gut and blood-brain barrier dysfunction underlying stress-induced anxiety and depression.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14091}, pmid = {36650644}, issn = {1755-5949}, abstract = {BACKGROUND: Most of the previous studies have demonstrated the potential antidepressive and anxiolytic role of prebiotic supplement in male subjects, yet few have females enrolled. Herein, we explored whether prebiotics administration during chronic stress prevented depression-like and anxiety-like behavior in a sex-specific manner and the mechanism of behavioral differences caused by sex.<br><br>METHODS: Female and male C57 BL/J mice on normal diet were supplemented with or without a combination of fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS) during 3- and 4-week chronic restraint stress (CRS) treatment, respectively. C57 BL/J mice on normal diet without CRS were used as controls. Behavior consequences, gut microbiota, dysfunction of gut and brain-blood barriers, and inflammatory profiles were measured.<br><br>RESULTS: In the 3rd week, FOS&#x2009;+&#x2009;GOS administration attenuated stress-induced anxiety-like behavior in female, but not in male mice, and the anxiolytic effects in males were observed until the 4th week. However, protective effects of prebiotics on CRS-induced depression were not observed. Changes in the gene expression of tight junction proteins in the distal colon and hippocampus, and decreased number of colon goblet cells following CRS were restored by prebiotics only in females. In both female and male mice, prebiotics alleviated stress-induced BBB dysfunction and elevation in pro-inflammatory cytokines levels, and modulated gut microbiota caused by stress. Furthermore, correlation analysis revealed that anxiety-like behaviors were significantly correlated with levels of pro-inflammatory cytokines and gene expression of tight junction proteins in the hippocampus of female mice, and the abundance of specific gut microbes was also correlated with anxiety-like behaviors, pro-inflammatory cytokines, and gene expression of tight junction proteins in the hippocampus of female mice.<br><br>CONCLUSION: Female mice were more vulnerable to stress and prebiotics than males. The gut microbiota, gut and blood-brain barrier, and inflammatory response may mediate the protective effects of prebiotics on anxiety-like behaviors in female mice.}, }
@article {pmid36650410, year = {2023}, author = {Zhang, J and Wang, X and Xu, B and Wu, Y and Lou, X and Shen, X}, title = {An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36650410}, issn = {1741-0444}, abstract = {The motor imagery brain-computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.}, }
@article {pmid36645915, year = {2023}, author = {Li, M and Zuo, H and Zhou, H and Xu, G and Qi, E}, title = {A study of action difference on motor imagery based on delayed matching posture task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb386}, pmid = {36645915}, issn = {1741-2552}, abstract = {OBJECTIVE: Motor imagery (MI)-based brain-computer interfaces (BCI) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively.<br><br>APPROACH: To explore action differences, this study proposes a novel paradigm named as Action Observation based Delayed Matching Posture Task (AO-DMPT). Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference.<br><br>MAIN RESULTS: Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p<0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28&#x3bc;V of grasping, 5.64&#x3bc;V and 5.25&#x3bc;V higher than the cutting and the writing, respectively (p<0.05).<br><br>SIGNIFICANCE: The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.}, }
@article {pmid36645913, year = {2023}, author = {Valencia, D and Leone, G and Keller, N and Mercier, PP and Alimohammad, A}, title = {Power-efficient in vivo brain-machine interfaces via brain-state estimation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb385}, pmid = {36645913}, issn = {1741-2552}, abstract = {OBJECTIVE: Advances in brain-machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will.<br><br>APPROACH: To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of an in vivo intention-aware interface via brain-state estimation.<br><br>MAIN RESULTS: It is shown that incorporating brain-state estimation reduces the in vivo power consumption and reduces total energy dissipation by over 1.8x compared to those of the current systems, enabling longer batter life for implanted circuits. The synthesized application-specific integrated circuit (ASIC) of the designed intention-aware multi-unit spike detection system in a standard 180-nm CMOS process occupies 0.03 mm[2]of silicon area and consumes 0.63 &#x3bc;W of power per channel, which is the least power consumption among the current in vivo ASIC realizations.<br><br>SIGNIFICANCE: The proposed interface is the first practical approach towards realizing asynchronous BMIs while reducing the power consumption of the BMI interface and enhancing neural decoding performance compared to those of the conventional synchronous BMIs.}, }
@article {pmid36644311, year = {2022}, author = {Sui, Y and Yu, H and Zhang, C and Chen, Y and Jiang, C and Li, L}, title = {Deep brain-machine interfaces: sensing and modulating the human deep brain.}, journal = {National science review}, volume = {9}, number = {10}, pages = {nwac212}, pmid = {36644311}, issn = {2053-714X}, abstract = {Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.}, }
@article {pmid36643889, year = {2023}, author = {Alharbi, H}, title = {Identifying Thematics in a Brain-Computer Interface Research.}, journal = {Computational intelligence and neuroscience}, volume = {2023}, number = {}, pages = {2793211}, pmid = {36643889}, issn = {1687-5273}, abstract = {This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.}, }
@article {pmid36534700, year = {2022}, author = {Zhang, L and Liu, C and Zhou, X and Zhou, H and Luo, S and Wang, Q and Yao, Z and Chen, JF}, title = {Neural representation and modulation of volitional motivation in response to escalating efforts.}, journal = {The Journal of physiology}, volume = {}, number = {}, pages = {}, doi = {10.1113/JP283915}, pmid = {36534700}, issn = {1469-7793}, abstract = {Task-dependent volitional control of the selected neural activity in the cortex is critical to neuroprosthetic learning to achieve reliable and robust control of the external device. The volitional control of neural activity is driven by a motivational factor (volitional motivation), which directly reinforces the target neurons via real-time biofeedback. However, in the absence of motor behaviour, how do we evaluate volitional motivation? Here, we defined the criterion (ΔF/F) of the calcium fluorescence signal in a volitionally controlled neural task, then escalated the efforts by progressively increasing the number of reaching the criterion or holding time after reaching the criterion. We devised calcium-based progressive threshold-crossing events (termed 'Calcium PTE') and calcium-based progressive threshold-crossing holding-time (termed 'Calcium PTH') for quantitative assessment of volitional motivation in response to progressively escalating efforts. Furthermore, we used this novel neural representation of volitional motivation to explore the neural circuit and neuromodulator bases for volitional motivation. As with behavioural motivation, chemogenetic activation and pharmacological blockade of the striatopallidal pathway decreased and increased, respectively, the breakpoints of the 'Calcium PTE' and 'Calcium PTH' in response to escalating efforts. Furthermore, volitional and behavioural motivation shared similar dopamine dynamics in the nucleus accumbens in response to trial-by-trial escalating efforts. In general, the development of a neural representation of volitional motivation may open a new avenue for smooth and effective control of brain-machine interface tasks. KEY POINTS: Volitional motivation is quantitatively evaluated by M1 neural activity in response to progressively escalating volitional efforts. The striatopallidal pathway and adenosine A2A receptor modulate volitional motivation in response to escalating efforts. Dopamine dynamics encode prediction signal for reward in response to repeated escalating efforts during motor and volitional conditioning. Mice learn to modulate neural activity to compensate for repeated escalating efforts in volitional control.}, }
@article {pmid36639665, year = {2023}, author = {Pichiorri, F and Toppi, J and de Seta, V and Colamarino, E and Masciullo, M and Tamburella, F and Lorusso, M and Cincotti, F and Mattia, D}, title = {Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {5}, pmid = {36639665}, issn = {1743-0003}, abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks.<br><br>METHODS: EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups.<br><br>RESULTS: Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections' distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients.<br><br>CONCLUSIONS: High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.}, }
@article {pmid36639237, year = {2023}, author = {Rubin, DB and Ajiboye, AB and Barefoot, L and Bowker, M and Cash, SS and Chen, D and Donoghue, JP and Eskandar, EN and Friehs, G and Grant, C and Henderson, JM and Kirsch, RF and Marujo, R and Masood, M and Mernoff, ST and Miller, JP and Mukand, JA and Penn, RD and Shefner, J and Shenoy, KV and Simeral, JD and Sweet, JA and Walter, BL and Williams, ZM and Hochberg, LR}, title = {Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System.}, journal = {Neurology}, volume = {}, number = {}, pages = {}, doi = {10.1212/WNL.0000000000201707}, pmid = {36639237}, issn = {1526-632X}, abstract = {BACKGROUND AND OBJECTIVES: Brain computer interfaces (BCIs) are being developed to restore mobility, communication, and functional independence to people with paralysis. Though supported by decades of preclinical data, the safety of chronically implanted microelectrode array BCIs in humans is unknown. We report safety results from the prospective, open-label, non-randomized BrainGate feasibility study (NCT00912041), the largest and longest-running clinical trial of an implanted BCI.<br><br>METHODS: Adults aged 18-75 with quadriparesis from spinal cord injury, brainstem stroke, or motor neuron disease were enrolled through seven clinical sites in the United States. Participants underwent surgical implantation of one or two microelectrode arrays in the motor cortex of the dominant cerebral hemisphere. The primary safety outcome was device-related serious adverse events requiring device explanation or resulting in death or permanently increased disability during the one-year post-implant evaluation period. Secondary outcomes include the type and frequency of other adverse events as well as the feasibility of the BrainGate system for controlling a computer or other assistive technologies.<br><br>RESULTS: From 2004 - 2021, fourteen adults enrolled in the BrainGate trial had devices surgically implanted. The average duration of device implantation was 872 days, yielding 12,203 days of safety experience. There were 68 device-related adverse events, including 6 device-related serious adverse events. The most common device-related adverse event was skin irritation around the percutaneous pedestal. There were no safety events that required device explantation, no unanticipated adverse device events, no intracranial infections, and no participant deaths or adverse events resulting in permanently increased disability related to the investigational device.<br><br>DISCUSSION: The BrainGate Neural Interface system has a safety record comparable to other chronically implanted medical devices. Given rapid recent advances in this technology and continued performance gains, these data suggest a favorable risk/benefit ratio in appropriately selected individuals to support ongoing research and development.<br><br>ClinicalTrials.gov Identifier: NCT00912041.<br><br>CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that the neurosurgically placed BrainGate Neural Interface system is associated with a low rate of SAEs defined as those requiring device explanation, resulting in death, or resulting in permanently increased disability during the one-year post implant period.}, }
@article {pmid36638268, year = {2023}, author = {Wu, J and Chen, C and Qin, C and Li, Y and Jiang, N and Yuan, Q and Duan, Y and Liu, M and Wei, X and Yu, Y and Zhuang, L and Wang, P}, title = {Mimicking the Biological Sense of Taste In Vitro Using a Taste Organoids-on-a-Chip System.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2206101}, doi = {10.1002/advs.202206101}, pmid = {36638268}, issn = {2198-3844}, abstract = {Thanks to the gustatory system, humans can experience the flavors in foods and drinks while avoiding the intake of some harmful substances. Although great advances in the fields of biotechnology, microfluidics, and nanotechnologies have been made in recent years, this astonishing recognition system can hardly be replaced by any artificial sensors designed so far. Here, taste organoids are coupled with an extracellular potential sensor array to form a novel bioelectronic organoid and developed a taste organoids-on-a-chip system (TOS) for highly mimicking the biological sense of taste ex vivo with high stability and repeatability. The taste organoids maintain key taste receptors expression after the third passage and high cell viability during 7 days of on-chip culture. Most importantly, the TOS not only distinguishs sour, sweet, bitter, and salt stimuli with great specificity, but also recognizes varying concentrations of the stimuli through an analytical method based on the extraction of signal features and principal component analysis. It is hoped that this bioelectronic tongue can facilitate studies in food quality controls, disease modelling, and drug screening.}, }
@article {pmid36637269, year = {2023}, author = {Hu, J and Wang, Y and Zhu, Y and Li, Y and Chen, J and Zhang, Y and Xu, D and Bai, R and Wang, L}, title = {Preoperative Brain Functional Connectivity Improve Predictive Accuracy of Outcomes After Revascularization in Moyamoya Disease.}, journal = {Neurosurgery}, volume = {92}, number = {2}, pages = {344-352}, doi = {10.1227/neu.0000000000002205}, pmid = {36637269}, issn = {1524-4040}, abstract = {BACKGROUND: In patients with moyamoya disease (MMD), focal impairments in cerebral hemodynamics are often inconsistent with patients' clinical prognoses. Evaluation of entire brain functional networks may enable predicting MMD outcomes after revascularization.<br><br>OBJECTIVE: To investigate whether preoperative brain functional connectivity could predict outcomes after revascularization in MMD.<br><br>METHODS: We included 34 patients with MMD who underwent preoperative MRI scanning and combined revascularization surgery. We used region of interest analyses to explore the differences in functional connectivity for 90 paired brain regions between patients who had favorable outcomes 1 year after surgery (no recurrent stroke, with improved preoperative symptoms, or modified Rankin Scale [mRS]) and those who had unimproved outcomes (recurrent stroke, persistent symptoms, or declined mRS). Variables, including age, body mass index, mRS at admission, Suzuki stage, posterior cerebral artery involvement, and functional connectivity with significant differences between the groups, were included in the discriminant function analysis to predict patient outcomes.<br><br>RESULTS: Functional connectivity between posterior cingulate cortex and paracentral lobule within the right hemisphere, and interhemispheric connection between superior parietal gyrus and middle frontal gyrus, precuneus and middle cingulate cortex, cuneus and precuneus, differed significantly between the groups (P < .001, false discovery rate corrected) and had the greatest discriminant function in the prediction model. Although clinical characteristics of patients with MMD showed great accuracy in predicting outcomes (64.7%), adding information on functional connections improved accuracy to 91.2%.<br><br>CONCLUSION: Preoperative functional connectivity derived from rs-fMRI may be an early hallmark for predicting patients' prognosis after revascularization surgery for MMD.}, }
@article {pmid36636754, year = {2023}, author = {Hudson, HM and Guggenmos, DJ and Azin, M and Vitale, N and McKenzie, KA and Mahnken, JD and Mohseni, P and Nudo, RJ}, title = {Broad Therapeutic Time Window for Driving Motor Recovery After TBI Using Activity-Dependent Stimulation.}, journal = {Neurorehabilitation and neural repair}, volume = {}, number = {}, pages = {15459683221145144}, doi = {10.1177/15459683221145144}, pmid = {36636754}, issn = {1552-6844}, abstract = {BACKGROUND: After an acquired injury to the motor cortex, the ability to generate skilled movements is impaired, leading to long-term motor impairment and disability. While rehabilitative therapy can improve outcomes in some individuals, there are no treatments currently available that are able to fully restore lost function.<br><br>OBJECTIVE: We previously used activity-dependent stimulation (ADS), initiated immediately after an injury, to drive motor recovery. The objective of this study was to determine if delayed application of ADS would still lead to recovery and if the recovery would persist after treatment was stopped.<br><br>METHODS: Rats received a controlled cortical impact over primary motor cortex, microelectrode arrays were implanted in ipsilesional premotor and somatosensory areas, and a custom brain-machine interface was attached to perform the ADS. Stimulation was initiated either 1, 2, or 3&#x2009;weeks after injury and delivered constantly over a 4-week period. An additional group was monitored for 8&#x2009;weeks after terminating ADS to assess persistence of effect. Results were compared to rats receiving no stimulation.<br><br>RESULTS: ADS was delayed up to 3&#x2009;weeks from injury onset and still resulted in significant motor recovery, with maximal recovery occurring in the 1-week delay group. The improvements in motor performance persisted for at least 8&#x2009;weeks following the end of treatment.<br><br>CONCLUSIONS: ADS is an effective method to treat motor impairments following acquired brain injury in rats. This study demonstrates the clinical relevance of this technique as it could be initiated in the post-acute period and could be explanted/ceased once recovery has occurred.}, }
@article {pmid36636584, year = {2022}, author = {Truong, MT and Liu, YC and Kohn, J and Chinnadurai, S and Zopf, DA and Tribble, M and Tanner, PB and Sie, K and Chang, KW}, title = {Integrated microtia and aural atresia management.}, journal = {Frontiers in surgery}, volume = {9}, number = {}, pages = {944223}, pmid = {36636584}, issn = {2296-875X}, abstract = {OBJECTIVES: To present recommendations for the coordinated evaluation and management of the hearing and reconstructive needs of patients with microtia and aural atresia.<br><br>METHODS: A national working group of 9 experts on microtia and atresia evaluated a working document on the evaluation and treatment of patients. Treatment options for auricular reconstruction and hearing habilitation were reviewed and integrated into a coordinated care timeline.<br><br>RESULTS: Recommendations were created for children with microtia and atresia, including diagnostic considerations, surgical and non-surgical options for hearing management and auricular reconstruction, and the treatment timeline for each option. These recommendations are based on the collective opinion of the group and are intended for otolaryngologists, audiologists, plastic surgeons, anaplastologists, and any provider caring for a patient with microtia and ear canal atresia. Close communication between atresia/hearing reconstruction surgeon and microtia repair surgeon is strongly recommended.}, }
@article {pmid36635340, year = {2023}, author = {Daly, I}, title = {Neural decoding of music from the EEG.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {624}, pmid = {36635340}, issn = {2045-2322}, abstract = {Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder. Specifically, we first used a joint EEG-fMRI paradigm to record brain activity while participants listened to music. We then used fMRI-informed EEG source localisation and a bi-directional long-term short term deep learning network to first extract neural information from the EEG related to music listening and then to decode and reconstruct the individual pieces of music an individual was listening to. We further validated our decoding model by evaluating its performance on a separate dataset of EEG-only recordings. We were able to reconstruct music, via our fMRI-informed EEG source analysis approach, with a mean rank accuracy of 71.8% ([Formula: see text], [Formula: see text]). Using only EEG data, without participant specific fMRI-informed source analysis, we were able to identify the music a participant was listening to with a mean rank accuracy of 59.2% ([Formula: see text], [Formula: see text]). This demonstrates that our decoding model may use fMRI-informed source analysis to aid EEG based decoding and reconstruction of acoustic information from brain activity and makes a step towards building EEG-based neural decoders for other complex information domains such as other acoustic, visual, or semantic information.}, }
@article {pmid36634598, year = {2022}, author = {Zhu, S and Hosni, SI and Huang, X and Wan, M and Borgheai, SB and McLinden, J and Shahriari, Y and Ostadabbas, S}, title = {A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces.}, journal = {Computers in biology and medicine}, volume = {153}, number = {}, pages = {106498}, doi = {10.1016/j.compbiomed.2022.106498}, pmid = {36634598}, issn = {1879-0534}, abstract = {Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain-computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of 71.1%±4.5% for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance (67.1%±7.5%). Compared to using either one of the graphic features (66.3%±6.5% for the eigenvalues and 65.9%±5.2% for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.}, }
@article {pmid36633302, year = {2023}, author = {Sample, M and Sattler, S and Boehlen, W and Racine, E}, title = {Brain-computer interfaces, disability, and the stigma of refusal: A factorial vignette study.}, journal = {Public understanding of science (Bristol, England)}, volume = {}, number = {}, pages = {9636625221141663}, doi = {10.1177/09636625