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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 29 May 2023 at 01:35 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: (bci OR (brain-computer OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)


RevDate: 2023-05-28

Novičić M, AM Savić (2023)

Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training.

Brain sciences, 13(5): pii:brainsci13050766.

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.

RevDate: 2023-05-27

Zhu S, Yang J, Ding P, et al (2023)

Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph.

Brain sciences, 13(5): pii:brainsci13050710.

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.

RevDate: 2023-05-27

Tan X, Wang D, Chen J, et al (2023)

Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.

Bioengineering (Basel, Switzerland), 10(5): pii:bioengineering10050609.

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.

RevDate: 2023-05-27

Ali MU, Kim KS, Kallu KD, et al (2023)

OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.

Bioengineering (Basel, Switzerland), 10(5): pii:bioengineering10050608.

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.

RevDate: 2023-05-27

Perpetuini D, Günal M, Chiou N, et al (2023)

Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface.

Bioengineering (Basel, Switzerland), 10(5): pii:bioengineering10050553.

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.

RevDate: 2023-05-26

Yang Z, Liu F, Li Z, et al (2023)

Histone lysine methyltransferase SMYD3 promotes oral squamous cell carcinoma tumorigenesis via H3K4me3-mediated HMGA2 transcription.

Clinical epigenetics, 15(1):92.

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.

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.

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.

RevDate: 2023-05-26

Nadra JG, Bengson JJ, Morales AB, et al (2023)

Attention Without Constraint: Alpha Lateralization in Uncued Willed Attention.

eNeuro pii:ENEURO.0258-22.2023 [Epub ahead of print].

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.

RevDate: 2023-05-26

Cui Y, Xie S, Xie X, et al (2023)

LDER: A classification framework based on ERP enhancement in RSVP task.

Journal of neural engineering [Epub ahead of print].

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. .

RevDate: 2023-05-26

Lycke R, Kim R, Zolotavin P, et al (2023)

Low-threshold, high-resolution, chronically stable intracortical microstimulation by ultraflexible electrodes.

Cell reports, 42(6):112554 pii:S2211-1247(23)00565-X [Epub ahead of print].

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.

RevDate: 2023-05-26

Haggie L, Schmid L, Röhrle O, et al (2023)

Linking cortex and contraction-Integrating models along the corticomuscular pathway.

Frontiers in physiology, 14:1095260.

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.

RevDate: 2023-05-26

Centeio R, Cabrita I, Schreiber R, et al (2023)

TMEM16A/F support exocytosis but do not inhibit Notch-mediated goblet cell metaplasia of BCi-NS1.1 human airway epithelium.

Frontiers in physiology, 14:1157704.

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.

RevDate: 2023-05-27

Yang G, Wang Y, Xu Z, et al (2023)

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.

Biosensors, 13(5):.

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.

RevDate: 2023-05-25

de Wauw CV, Riecke L, Goebel R, et al (2023)

Talking with hands and feet: Selective somatosensory attention and fMRI enable robust and convenient brain-based communication.

NeuroImage pii:S1053-8119(23)00323-3 [Epub ahead of print].

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.

RevDate: 2023-05-27

Deverett B (2023)

Anesthesia for non-traditional consciousness.

Frontiers in human neuroscience, 17:1146242.

RevDate: 2023-05-27

Baker JL, Toth R, Deli A, et al (2023)

Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.

International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering, 2023:10123754.

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.

RevDate: 2023-05-27

Dale R, O'sullivan TD, Howard S, et al (2023)

System Derived Spatial-Temporal CNN for High-Density fNIRS BCI.

IEEE open journal of engineering in medicine and biology, 4:85-95.

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.

RevDate: 2023-05-25

Jia H, Feng F, Caiafa CF, et al (2023)

Multi-class Classification of Upper Limb Movements with Filter Bank Task-related Component Analysis.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

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.

RevDate: 2023-05-26

Rathee G, Kerrache CA, M Bilal (2023)

An Accurate and Inter-Operatable Fuzzy-based System using Genetic and Canonical Correlation Analysis Methods in Internet-of-Brain Things.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

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.

RevDate: 2023-05-25

Zhu Z, Han J, Zhu H, et al (2023)

Individualized targeting is warranted in subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression: A tractography analysis.

Human brain mapping [Epub ahead of print].

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.

RevDate: 2023-05-25

Li M, Wei R, Zhang Z, et al (2023)

CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation.

Cyborg and bionic systems (Washington, D.C.), 4:0024.

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.

RevDate: 2023-05-23

Xu P, Huang S, Krumm BE, et al (2023)

Structural genomics of the human dopamine receptor system.

Cell research [Epub ahead of print].

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.

RevDate: 2023-05-24

Li D, Wang J, Xu J, et al (2023)

Cross-Channel Specific-Mutual Feature Transfer Learning for Motor Imagery EEG Signals Decoding.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

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.

RevDate: 2023-05-22

Zhang Z, Feng P, Oprea A, et al (2023)

Calibration-Free and Hardware-Efficient Neural Spike Detection for Brain Machine Interfaces.

IEEE transactions on biomedical circuits and systems, PP: [Epub ahead of print].

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.

RevDate: 2023-05-23

Rao S, Huang S, Liu X, et al (2023)

Control of Polymers' Amorphous-crystalline Transition for Hydrogel Bioelectronics Miniaturization and Multifunctional Integration.

Research square

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).

RevDate: 2023-05-23

Syrov N, Yakovlev L, Miroshnikov A, et al (2023)

Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI.

Frontiers in human neuroscience, 17:1180056.

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.

RevDate: 2023-05-23

Wang X, Dai X, Liu Y, et al (2023)

Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.

Frontiers in human neuroscience, 17:1175399.

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.

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.

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.

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.

RevDate: 2023-05-23

Feng B, Yu T, Wang H, et al (2023)

Editorial: Machine learning and deep learning in biomedical signal analysis.

Frontiers in human neuroscience, 17:1183840.

RevDate: 2023-05-22

Ni P, Zhou C, Liang S, et al (2023)

YBX1-Mediated DNA Methylation-Dependent SHANK3 Expression in PBMCs and Developing Cortical Interneurons in Schizophrenia.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Epub ahead of print].

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.

RevDate: 2023-05-22

Wang X, Zhang A, Yu Q, et al (2023)

Single-Cell RNA Sequencing and Spatial Transcriptomics Reveal Pathogenesis of Meningeal Lymphatic Dysfunction after Experimental Subarachnoid Hemorrhage.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Epub ahead of print].

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.

RevDate: 2023-05-20

Zhao Y, Zeng H, Zheng H, et al (2023)

A bidirectional interaction-based hybrid network architecture for EEG cognitive recognition.

Computer methods and programs in biomedicine, 238:107593 pii:S0169-2607(23)00258-4 [Epub ahead of print].

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.

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.

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 + EEGNet and CapsNet + EEGNet were verified. The proposed method achieved average accuracies of 78.76% (GCN + EEGNet) and 77.58% (CapsNet + EEGNet) on FAAD and 55.38% (GCN + EEGNet) and 55.10% (CapsNet + EEGNet) on SEED, outperforming the hybrid networks without the bidirectional interaction strategy.

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.

RevDate: 2023-05-20

Chen J, Zhang Y, Pan Y, et al (2023)

A transformer-based deep neural network model for SSVEP classification.

Neural networks : the official journal of the International Neural Network Society, 164:521-534 pii:S0893-6080(23)00231-9 [Epub ahead of print].

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.

RevDate: 2023-05-20

Hong W, Liang P, Pan Y, et al (2023)

Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder.

Journal of behavioral addictions [Epub ahead of print].

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).

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.

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 profile 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.

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.

RevDate: 2023-05-22

Li J, Qi Y, G Pan (2023)

Phase-amplitude coupling-based adaptive filters for neural signal decoding.

Frontiers in neuroscience, 17:1153568.

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.

RevDate: 2023-05-22

Marcos-Martínez D, Santamaría-Vázquez E, Martínez-Cagigal V, et al (2023)

ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces.

Computers in biology and medicine, 160:107011.

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.

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.

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.

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.

RevDate: 2023-05-18

Ai J, Meng J, Mai X, et al (2023)

BCI Control of a Robotic arm based on SSVEP with Moving Stimuli for Reach and grasp Tasks.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

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.

RevDate: 2023-05-19
CmpDate: 2023-05-19

Murphy RR (2023)

Sci-fi imagines how good brain-machine interfaces will amplify bad choices.

Science robotics, 8(78):eadi2192.

Machine Man and The Andromeda Evolution explore personal and societal ramifications of brain-machine interfaces.

RevDate: 2023-05-18

Phillips MM, Pavlyk I, Allen M, et al (2023)

Correction: A role for macrophages under cytokine control in mediating resistance to ADI-PEG20 (pegargiminase) in ASS1-deficient mesothelioma.

RevDate: 2023-05-16

O'Leary K (2023)

MRI decoders translate thoughts into words.

RevDate: 2023-05-16

Niu L, Bin J, Wang JKS, et al (2023)

Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.

Medical & biological engineering & computing [Epub ahead of print].

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.

RevDate: 2023-05-19

Al-Nafjan A, Aldayel M, A Kharrat (2023)

Systematic Review and Future Direction of Neuro-Tourism Research.

Brain sciences, 13(4):.

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.

RevDate: 2023-05-16

Vanutelli ME, Salvadore M, C Lucchiari (2023)

BCI Applications to Creativity: Review and Future Directions, from little-c to C[2].

Brain sciences, 13(4):.

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.

RevDate: 2023-05-16

Lakshminarayanan K, Shah R, Daulat SR, et al (2023)

Evaluation of EEG Oscillatory Patterns and Classification of Compound Limb Tactile Imagery.

Brain sciences, 13(4):.

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.

RevDate: 2023-05-16

Yang J, Wang M, Lv Y, et al (2023)

Cortical Layer Markers Expression and Increased Synaptic Density in Interstitial Neurons of the White Matter from Drug-Resistant Epilepsy Patients.

Brain sciences, 13(4):.

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.

RevDate: 2023-05-16

Nie A, Y Wu (2023)

Differentiation of the Contribution of Familiarity and Recollection to the Old/New Effects in Associative Recognition: Insight from Semantic Relation.

Brain sciences, 13(4):.

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.

RevDate: 2023-05-17
CmpDate: 2023-05-17

Yan W, He B, Zhao J, et al (2023)

Frequency Domain Filtering Method for SSVEP-EEG Preprocessing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 31:2079-2089.

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.

RevDate: 2023-05-16

Sahli F, Sahli H, Trabelsi O, et al (2023)

Peer Verbal Encouragement Enhances Offensive Performance Indicators in Handball Small-Sided Games.

Children (Basel, Switzerland), 10(4): pii:children10040680.

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.

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).

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.

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.

RevDate: 2023-05-15

Liang F, Yu S, Pang S, et al (2023)

Non-human primate models and systems for gait and neurophysiological analysis.

Frontiers in neuroscience, 17:1141567.

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&V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&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.

RevDate: 2023-05-15

Xu F, Wang C, Yu X, et al (2023)

One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain.

International journal of neural systems [Epub ahead of print].

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.

RevDate: 2023-05-14

Bu Y, Harrington DL, Lee RR, et al (2023)

Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.

Cerebral cortex (New York, N.Y. : 1991) pii:7161766 [Epub ahead of print].

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.

RevDate: 2023-05-15

Barradas-Chacón LA, Brunner C, SC Wriessnegger (2023)

Stylized faces enhance ERP features used for the detection of emotional responses.

Frontiers in human neuroscience, 17:1160800.

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.

RevDate: 2023-05-15

Jia X, Wang W, Liang J, et al (2023)

Application of amide proton transfer imaging to pretreatment risk stratification of childhood neuroblastoma: comparison with neuron-specific enolase.

Quantitative imaging in medicine and surgery, 13(5):3001-3012.

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).

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.

RESULTS: Thirty-four cases (mean age, 38.6±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%±1.27%) than in the non-high-risk group (3.88%±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±97.14 ng/mL) and non-high-risk groups (41.45±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).

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.

RevDate: 2023-05-15
CmpDate: 2023-05-15

Knierim MT, Bleichner MG, P Reali (2023)

A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.

Sensors (Basel, Switzerland), 23(9):.

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.

RevDate: 2023-05-15
CmpDate: 2023-05-15

de Brito Guerra TC, Nóbrega T, Morya E, et al (2023)

Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery.

Sensors (Basel, Switzerland), 23(9):.

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.

RevDate: 2023-05-15
CmpDate: 2023-05-15

Ortega-Rodríguez J, Gómez-González JF, E Pereda (2023)

Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals.

Sensors (Basel, Switzerland), 23(9):.

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.

RevDate: 2023-05-16
CmpDate: 2023-05-16

Khrulev AE, Kuryatnikova KM, Belova АN, et al (2022)

Modern Rehabilitation Technologies of Patients with Motor Disorders at an Early Rehabilitation of Stroke (Review).

Sovremennye tekhnologii v meditsine, 14(6):64-78.

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.

RevDate: 2023-05-12

Saini M, U Satija (2023)

State of art mental tasks classification based on electroencephalogram: a review.

Physiological measurement [Epub ahead of print].

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.

RevDate: 2023-05-12

Zhang F, Yang Y, Xin Y, et al (2023)

Efficacy of different strategies of responsive neurostimulation on seizure control and their association with acute neurophysiological effects in rats.

Epilepsy & behavior : E&B, 143:109212 pii:S1525-5050(23)00131-2 [Epub ahead of print].

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.

RevDate: 2023-05-12

Wang Z, Yang L, Wang M, et al (2023)

Motor imagery and action observation induced electroencephalographic activations to guide subject-specific training paradigm: a pilot study.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

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.

RevDate: 2023-05-12

Zhao J, Shi Y, Liu W, et al (2023)

A Hybrid Method Fusing Frequency Recognition with Attention Detection to Enhance an Asynchronous Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

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.

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.

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).

CONCLUSION: The results suggest that the proposed ADFR-DS method can enhance asynchronous SSVEP-based BCI systems.

RevDate: 2023-05-13

Li M, Cheng S, Fan J, et al (2022)

Disarrangement and reorganization of the hippocampal functional connectivity during the spatial path adjustment of pigeons.

BMC zoology, 7(1):54.

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.

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).

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.

RevDate: 2023-05-13

Sun H, Li C, H Zhang (2023)

Design of virtual BCI channels based on informer.

Frontiers in human neuroscience, 17:1150316.

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.

RevDate: 2023-05-13

Albahri AS, Al-Qaysi ZT, Alzubaidi L, et al (2023)

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.

International journal of telemedicine and applications, 2023:7741735.

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.

RevDate: 2023-05-11

Cao K, Qiu L, Lu X, et al (2023)

Microglia modulate general anesthesia through P2Y12 receptor.

Current biology : CB pii:S0960-9822(23)00529-8 [Epub ahead of print].

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.

RevDate: 2023-05-13

Shi Y, Ananthakrishnan A, Oh S, et al (2022)

A Neuromorphic Brain Interface based on RRAM Crossbar Arrays for High Throughput Real-time Spike Sorting.

IEEE transactions on electron devices, 69(4):2137-2144.

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.

RevDate: 2023-05-11

Zhou L, Xu Y, Song F, et al (2023)

The effect of TENS on sleep: A pilot study.

Sleep medicine, 107:126-136 pii:S1389-9457(23)00165-X [Epub ahead of print].

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.

OBJECTIVE: This work aims to explore whether and how TENS modulate sleep and the effect of stimulation waveforms on sleep.

METHODS: Forty-five healthy subjects participated in this study. Electroencephalography (EEG) data were recorded before and after four mode low-frequency (1 Hz) TENS with different waveforms, which were formed by superimposing sine waves of different high frequencies (60-210 Hz) and low frequencies (1-6 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 Hz) and low frequencies (1-6 Hz). Mode 2 (M2) is made up of high frequencies (60-210 Hz) and low frequencies (1-6 Hz). Mode 3 (M3) consists of high frequencies (110-160 Hz) and low frequencies (1-6 Hz), while mode 4 (M4) is composed of high frequencies (160-210 Hz) and low frequencies (1-6 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 mA to 7 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.

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.

CONCLUSION: These results indicated that low-frequency TENS (1 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.

RevDate: 2023-05-11

Wang P, Li Z, Gong P, et al (2023)

MTRT: Motion Trajectory Reconstruction Transformer for EEG-based BCI Decoding.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

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.

RevDate: 2023-05-11

Padfield N, Agius Anastasi A, Camilleri T, et al (2023)

BCI-controlled wheelchairs: end-users' perceptions, needs, and expectations, an interview-based study.

Disability and rehabilitation. Assistive technology [Epub ahead of print].

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.

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.

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.

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.

RevDate: 2023-05-10

Song S, Fallegger F, Trouillet A, et al (2023)

Deployment of an electrocorticography system with a soft robotic actuator.

Science robotics, 8(78):eadd1002.

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.

RevDate: 2023-05-11

Herring EZ, Graczyk EL, Memberg WD, et al (2023)

Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration after Tetraplegia.

medRxiv : the preprint server for health sciences pii:2023.04.24.23288977.

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.

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.

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.

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.

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.

RevDate: 2023-05-10

Shen Q, Fu S, Jiang X, et al (2023)

Factual and counterfactual learning in major adolescent depressive disorder, evidence from an instrumental learning study.

Psychological medicine pii:S0033291723001307 [Epub ahead of print].

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.

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).

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.

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.

RevDate: 2023-05-11
CmpDate: 2023-05-11

Emigh B, Grigorian A, Dilday J, et al (2023)

Risk factors and outcomes in pediatric blunt cardiac injuries.

Pediatric surgery international, 39(1):195.

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.

METHODS: A retrospective review was performed using the Trauma Quality Improvement Program (TQIP) database from 2017 to 2019. All patients ≤ 16 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.

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 = 0.158). The strongest independent risk factors for BCI were pulmonary contusions (OR 15.4, p < 0.001) and hemothorax (OR 8.9, p < 0.001).

CONCLUSIONS: Following trauma, the presence of pulmonary contusions or hemothorax should trigger additional screening investigations specific for BCI in pediatric patients.

RevDate: 2023-05-09

Guan C, Aflalo TN, Kadlec K, et al (2023)

Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex.

Journal of neural engineering [Epub ahead of print].

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.

RevDate: 2023-05-11
CmpDate: 2023-05-11

Wang J, Yin C, Pan Y, et al (2023)

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 of neuroinflammation, 20(1):109.

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.

METHODS: The mouse chronic post-ischemic pain (CPIP) model 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.

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-κB 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-κB activation. Specific overexpression of CXCL13 in SCDH neurons is sufficient to induce persistent mechanical allodynia in naïve mice.

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.

RevDate: 2023-05-11
CmpDate: 2023-05-11

Su W, Ju J, Gu M, et al (2023)

SARS-CoV-2 envelope protein triggers depression-like behaviors and dysosmia via TLR2-mediated neuroinflammation in mice.

Journal of neuroinflammation, 20(1):110.

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).

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.

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-1β, TNF-α, IL-6, CCL2, MMP2 and CSF1 were upregulated in both cortex and hippocampus, whereas IL-1β, 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.

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.

RevDate: 2023-05-08

Li H, Xu G, Li Z, et al (2023)

A Precise Frequency Recognition Method of Short-time SSVEP Signals Based on Signal Extension.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

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.

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.

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.

CONCLUSION: The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.

RevDate: 2023-05-09

Wu C, Shang HF, Wang YJ, et al (2023)

Cingulate protein arginine methyltransferases 1 regulates peripheral hypersensitivity via fragile X messenger ribonucleoprotein.

Frontiers in molecular neuroscience, 16:1153870.

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.

RevDate: 2023-05-09
CmpDate: 2023-05-08

Catalán JM, Trigili E, Nann M, et al (2023)

Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs).

Journal of neuroengineering and rehabilitation, 20(1):61.

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.

METHODS: Ten impaired participants (5 males and 5 females, mean age 52 ± 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 s and a reliable control in case it remained below 5 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.

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 s in case of the exoskeleton and bellow 5s in case of the external manipulator).

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.

RevDate: 2023-05-06

King BJ, Read GJM, PM Salmon (2023)

Identifying risk controls for future advanced brain-computer interfaces: A prospective risk assessment approach using work domain analysis.

Applied ergonomics, 111:104028 pii:S0003-6870(23)00066-2 [Epub ahead of print].

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.

RevDate: 2023-05-06

Li XY, Bao YF, Xie JJ, et al (2023)

Application Value of Serum Neurofilament Light Protein for Disease Staging in Huntington's Disease.

Movement disorders : official journal of the Movement Disorder Society [Epub ahead of print].

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.

METHODS: Serum NfL (sNfL) levels were quantified using an ultrasensitive immunoassay. Participants were assessed by clinical scales and 7.0 T magnetic resonance imaging. Longitudinal samples and clinical data were obtained.

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 < 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.

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. © 2023 International Parkinson and Movement Disorder Society.

RevDate: 2023-05-06

Neumann WJ, Gilron R, Little S, et al (2023)

Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation.

Movement disorders : official journal of the Movement Disorder Society [Epub ahead of print].

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.

RevDate: 2023-05-08
CmpDate: 2023-05-08

Ni RJ, Wang YY, Gao TH, et al (2023)

Depletion of microglia with PLX3397 attenuates MK-801-induced hyperactivity associated with regulating inflammation-related genes in the brain.

Zoological research, 44(3):543-555.

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.

RevDate: 2023-05-05

Meng L, Jiang X, Huang J, et al (2023)

EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

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.

RevDate: 2023-05-05

Wang J, Wang T, Liu H, et al (2023)

Flexible Electrodes for Brain-Computer Interface System.

Advanced materials (Deerfield Beach, Fla.) [Epub ahead of print].

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.

RevDate: 2023-05-04

Won K, Kim H, Gwon D, et al (2023)

Can vibrotactile stimulation and tDCS help inefficient BCI users?.

Journal of neuroengineering and rehabilitation, 20(1):60.

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.

RevDate: 2023-05-04

Batistić L, Lerga J, I Stanković (2023)

Detection of motor imagery based on short-term entropy of time-frequency representations.

Biomedical engineering online, 22(1):41.

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.

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.

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.

RevDate: 2023-05-04

Ziai Y, Zargarian SS, Rinoldi C, et al (2023)

Conducting polymer-based nanostructured materials for brain-machine interfaces.

Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology [Epub ahead of print].

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.

RevDate: 2023-05-04

Zhu L, Wang M, Liu Y, et al (2023)

Single-microvessel occlusion produces lamina-specific microvascular flow vasodynamics and signs of neurodegenerative change.

Cell reports, 42(5):112469 pii:S2211-1247(23)00480-1 [Epub ahead of print].

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.

RevDate: 2023-05-04

Wang Y, Zhao T, Jiao Y, et al (2023)

Silicate Nanoplatelets Promotes Neuronal Differentiation of Neural Stem Cells and Restoration of Spinal Cord Injury.

Advanced healthcare materials [Epub ahead of print].

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.

RevDate: 2023-05-04

Kosal M, J Putney (2023)

Neurotechnology and international security: Predicting commercial and military adoption of brain-computer interfaces (BCIs) in the United States and China.

Politics and the life sciences : the journal of the Association for Politics and the Life Sciences, 42(1):81-103.

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.

RevDate: 2023-05-04

Zhang X, Wang W, Zhang X, et al (2023)

Normal glymphatic system function in patients with new daily persistent headache using diffusion tensor image analysis along the perivascular space.

Headache [Epub ahead of print].

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.

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.

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.

RESULTS: In total, 27 patients with NDPH (14 males, 13 females; age [mean ± standard deviation (SD)]: 36.6 ± 20.6) and 33 healthy controls (15 males, 18 females; age [mean ± SD]: 36.0 ± 10.8) were included in the analysis. No significant differences between groups were observed in the left ALPS index (1.583 ± 0.182 vs. 1.586 ± 0.175, mean difference = 0.003, 95% confidence interval [CI] of difference = -0.089 to 0.096, p = 0.942), or right ALPS index (1.578 ± 0.230 vs. 1.559 ± 0.206, mean difference = -0.027, 95% CI of difference = -0.132 to 0.094, p = 0.738). Additionally, ALPS indexes were not correlated with clinical characteristics or neuropsychiatric scores.

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.

RevDate: 2023-05-04

Zhang Z, Zhao X, Ma Y, et al (2023)

[Ethics considerations on brain-computer interface technology].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 40(2):358-364.

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.

RevDate: 2023-05-04

Li H, Ji H, Yu J, et al (2023)

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI.

Frontiers in neuroscience, 17:1125230.

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.

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.

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%.

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.

RevDate: 2023-05-04

Zhu Y, Wang C, Li J, et al (2023)

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.

Frontiers in neurology, 14:1125172.

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.

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.

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.

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., identifier: CRD42022337776.

RevDate: 2023-05-04

Abdelrahman Z, Wang X, Wang D, et al (2023)

Identification of novel pathways and immune profiles related to sarcopenia.

Frontiers in medicine, 10:928285.

INTRODUCTION: Sarcopenia is a progressive deterioration of skeletal muscle mass strength and function.

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.

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.

CONCLUSION: This study provides new insight into sarcopenia's cellular and immunological prospects and evaluates the age and sarcopenia-related modifications of skeletal muscle.

RevDate: 2023-05-03

Ullah A, Liu Y, Wang Y, et al (2023)

Gender Differences in Taste Sensations Based on Frequency Analysis of Surface Electromyography.

Perceptual and motor skills [Epub ahead of print].

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.

RevDate: 2023-05-03

Gilbert F, Ienca M, M Cook (2023)

How I became myself after merging with a computer: Does human-machine symbiosis raise human rights issues?.

Brain stimulation pii:S1935-861X(23)01760-6 [Epub ahead of print].

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.

RevDate: 2023-05-03

Jia K, Goebel R, Z Kourtzi (2023)

Ultra-High Field Imaging of Human Visual Cognition.

Annual review of vision science [Epub ahead of print].

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 for revised estimates.

RevDate: 2023-05-04

He Y, Tang Z, Sun G, et al (2023)

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.

JMIR mHealth and uHealth, 11:e44855 pii:v11i1e44855.

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.

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.

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.

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).

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.


RevDate: 2023-05-04
CmpDate: 2023-05-04

Coelho HRS, Neves SC, Menezes JNS, et al (2023)

Cell therapy with adipose tissue-derived human stem cells in the urinary bladder improves detrusor contractility and reduces voiding residue.

Brazilian journal of biology = Revista brasleira de biologia, 83:e268540 pii:S1519-69842023000100462.

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.

RevDate: 2023-05-03

Deo DR, Willett FR, Avansino DT, et al (2023)

Translating deep learning to neuroprosthetic control.

bioRxiv : the preprint server for biology pii:2023.04.21.537581.

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.


ESP Quick Facts

ESP Origins

In the early 1990's, Robert Robbins was a faculty member at Johns Hopkins, where he directed the informatics core of GDB — the human gene-mapping database of the international human genome project. To share papers with colleagues around the world, he set up a small paper-sharing section on his personal web page. This small project evolved into The Electronic Scholarly Publishing Project.

ESP Support

In 1995, Robbins became the VP/IT of the Fred Hutchinson Cancer Research Center in Seattle, WA. Soon after arriving in Seattle, Robbins secured funding, through the ELSI component of the US Human Genome Project, to create the original ESP.ORG web site, with the formal goal of providing free, world-wide access to the literature of classical genetics.

ESP Rationale

Although the methods of molecular biology can seem almost magical to the uninitiated, the original techniques of classical genetics are readily appreciated by one and all: cross individuals that differ in some inherited trait, collect all of the progeny, score their attributes, and propose mechanisms to explain the patterns of inheritance observed.

ESP Goal

In reading the early works of classical genetics, one is drawn, almost inexorably, into ever more complex models, until molecular explanations begin to seem both necessary and natural. At that point, the tools for understanding genome research are at hand. Assisting readers reach this point was the original goal of The Electronic Scholarly Publishing Project.

ESP Usage

Usage of the site grew rapidly and has remained high. Faculty began to use the site for their assigned readings. Other on-line publishers, ranging from The New York Times to Nature referenced ESP materials in their own publications. Nobel laureates (e.g., Joshua Lederberg) regularly used the site and even wrote to suggest changes and improvements.

ESP Content

When the site began, no journals were making their early content available in digital format. As a result, ESP was obliged to digitize classic literature before it could be made available. For many important papers — such as Mendel's original paper or the first genetic map — ESP had to produce entirely new typeset versions of the works, if they were to be available in a high-quality format.

ESP Help

Early support from the DOE component of the Human Genome Project was critically important for getting the ESP project on a firm foundation. Since that funding ended (nearly 20 years ago), the project has been operated as a purely volunteer effort. Anyone wishing to assist in these efforts should send an email to Robbins.

ESP Plans

With the development of methods for adding typeset side notes to PDF files, the ESP project now plans to add annotated versions of some classical papers to its holdings. We also plan to add new reference and pedagogical material. We have already started providing regularly updated, comprehensive bibliographies to the ESP.ORG site.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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Papers in Classical Genetics

The ESP began as an effort to share a handful of key papers from the early days of classical genetics. Now the collection has grown to include hundreds of papers, in full-text format.

Digital Books

Along with papers on classical genetics, ESP offers a collection of full-text digital books, including many works by Darwin (and even a collection of poetry — Chicago Poems by Carl Sandburg).


ESP now offers a much improved and expanded collection of timelines, designed to give the user choice over subject matter and dates.


Biographical information about many key scientists.

Selected Bibliographies

Bibliographies on several topics of potential interest to the ESP community are now being automatically maintained and generated on the ESP site.

ESP Picks from Around the Web (updated 07 JUL 2018 )