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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 29 Sep 2023 at 01:39 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: 2023-09-27

Csaky R, van Es MWJ, Jones OP, et al (2023)

Group-level brain decoding with deep learning.

Human brain mapping [Epub ahead of print].

Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group-level models to outperform subject-specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between-subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group-level models to perform better than subject-level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (

RevDate: 2023-09-26

Chen B, Jiang L, Lu G, et al (2023)

Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks.

Cerebral cortex (New York, N.Y. : 1991) pii:7281875 [Epub ahead of print].

Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.

RevDate: 2023-09-25

Feng Z, Wang S, Qian L, et al (2023)

μ-STAR: A novel framework for spatio-temporal M/EEG source imaging optimized by microstates.

NeuroImage pii:S1053-8119(23)00523-2 [Epub ahead of print].

Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. In order to address this highly ill-posed problem, conventional source imaging models adopted spatio-temporal constraints that assume spatial stability of the source activities, neglecting the transient characteristics of M/EEG. In this work, a novel source imaging method μ-STAR that includes a microstate analysis and a spatio-temporal Bayesian model was introduced to address this problem. Specifically, the microstate analysis was applied to achieve automatic determination of time window length with quasi-stable source activity pattern for optimal reconstruction of source dynamics. Then a user-specific spatial prior and data-driven temporal basis functions were utilized to characterize the spatio-temporal information of sources within each state. The solution of the source reconstruction was obtained through a computationally efficient algorithm based upon variational Bayesian and convex analysis. The performance of the μ-STAR was first assessed through numerical simulations, where we found that the determination and inclusion of optimal temporal length in the spatio-temporal prior significantly improved the performance of source reconstruction. More importantly, the μ-STAR model achieved robust performance under various settings (i.e., source numbers/areas, SNR levels, and source depth) with fast convergence speed compared with five widely-used benchmark models (including wMNE, STV, SBL, BESTIES, & SI-STBF). Additional validations on real data were then performed on two publicly-available datasets (including block-design face-processing ERP and continuous resting-state EEG). The reconstructed source activities exhibited spatial and temporal neurophysiological plausible results consistent with previously-revealed neural substrates, thereby further proving the feasibility of the μ-STAR model for source imaging in various applications.

RevDate: 2023-09-25

Iivanainen J, Carter TR, Trumbo M, et al (2023)

Single-trial classification of evoked responses to auditory tones using OPM- and SQUID-MEG.

Journal of neural engineering [Epub ahead of print].

Optically pumped magnetometers (OPMs) are emerging as a near-room-temperature alternative to superconducting quantum interference devices (SQUIDs) for magnetoencephalography (MEG). In contrast to SQUIDs, OPMs can be placed in a close proximity to subject's scalp potentially increasing the signal-to-noise ratio and spatial resolution of MEG. However, experimental demonstrations of these suggested benefits are still scarce. Here, to compare a 24-channel OPM-MEG system to a commercial whole-head SQUID system in a data-driven way, we quantified their performance in classifying single-trial evoked responses. Approach: We measured evoked responses to three auditory tones in six participants using both OPM- and SQUID-MEG systems. We performed pairwise temporal classification of the single-trial responses with linear discriminant analysis as well as multiclass classification with both EEGNet convolutional neural network and xDAWN decoding. Main results: OPMs provided higher classification accuracies than SQUIDs having a similar coverage of the left hemisphere of the participant. However, the SQUID sensors covering the whole helmet had classification scores larger than those of OPMs for two of the tone pairs, demonstrating the benefits of a whole-head measurement. Significance: The results demonstrate that the current OPM-MEG system provides high-quality data about the brain with room for improvement for high bandwidth non-invasive brain-computer interfacing. .

RevDate: 2023-09-25

Kaongoen N, Choi J, Choi JW, et al (2023)

The future of wearable EEG: A review of ear-EEG technology and its applications.

Journal of neural engineering [Epub ahead of print].

This review paper provides a comprehensive overview of ear-EEG technology, which involves recording electroencephalogram (EEG) signals from electrodes placed in or around the ear, and its applications in the field of neural engineering. Approach: We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field. Main Results: Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring. Significance: This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.

RevDate: 2023-09-25

Tresols JJT, Chanel CPC, F Dehais (2023)

POMDP-BCI: A Benchmark of (re)active BCI using POMDP to Issue Commands.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

OBJECTIVE: Past research in Brain-Computer Interfaces (BCI) have presented different decoding algorithms for different modalities. Meanwhile, highly specific decision making processes have been developed for some of these modalities, while others lack such a component in their classic pipeline. The present work proposes a model based on Partially Observable Markov Decission Process (POMDP) that works as a high-level decision making framework for three different active/reactive BCI modalities.

METHODS: We tested our approach on three different BCI modalities using publicly available datasets. We compared the general POMDP model as a decision making process with state of the art methods for each BCI modality. Accuracy, false positive (FP) trials, no-action (NA) trials and average decision time are presented as metrics.

RESULTS: Our results show how the presented POMDP models achieve comparable or better performance to the presented baseline methods, while being usable for the three proposed experiments without significant changes. Crucially, it offers the possibility of taking no-action (NA) when the decoding does not perform well.

CONCLUSION: The present work implements a flexible POMDP model that acts as a sequential decision framework for BCI systems that lack such a component, and perform comparably to those that include it.

SIGNIFICANCE: We believe the proposed POMDP framework provides several interesting properties for future BCI developments, mainly the generalizability to any BCI modality and the possible integration of other physiological or brain data pipelines under a unified decision-making framework.

RevDate: 2023-09-27
CmpDate: 2023-09-26

Khan NN, Sweet T, Harvey CA, et al (2023)

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation.

Journal of visualized experiments : JoVE.

Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI systems are slow, making collecting sufficient data for validation a time-consuming task for end users and experimenters alike. Yet without sufficient data, the random variation in performance can lead to false inferences about how well a BCI is working for a particular user. For example, P300 spellers commonly operate around 1-5 characters per minute. To estimate accuracy with a 5% resolution requires 20 characters (4-20 min). Despite this time investment, the confidence bounds for accuracy from 20 characters can be as much as ±23% depending on observed accuracy. A previously published method, Classifier-Based Latency Estimation (CBLE), was shown to be highly correlated with BCI accuracy. This work presents a protocol for using CBLE to predict a user's P300 speller accuracy from relatively few characters (~3-8) of typing data. The resulting confidence bounds are tighter than those produced by traditional methods. The method can thus be used to estimate BCI performance more quickly and/or more accurately.

RevDate: 2023-09-26

Liu T, A Ye (2023)

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems.

Frontiers in neuroscience, 17:1251968.

BACKGROUND: For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.

METHODS: The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.

RESULTS: The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.

CONCLUSION: The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

RevDate: 2023-09-26

Huang Z, Liao Z, Ou G, et al (2023)

Authentication using c-VEP evoked in a mild-burdened cognitive task.

Frontiers in human neuroscience, 17:1240451.

In recent years, more and more researchers are devoting themselves to the studies about authentication based on biomarkers. Among a wide variety of biomarkers, code-modulated visual evoked potential (c-VEP) has attracted increasing attention due to its significant role in the field of brain-computer interface. In this study, we designed a mild-burdened cognitive task (MBCT), which can check whether participants focus their attention on the visual stimuli that evoke c-VEP. Furthermore, we investigated the authentication based on the c-VEP evoked in the cognitive task by introducing a deep learning method. Seventeen participants were recruited to take part in the MBCT experiments including two sessions, which were carried out on two different days. The c-VEP signals from the first session were extracted to train the authentication deep models. The c-VEP data of the second session were used to verify the models. It achieved a desirable performance, with the average accuracy and F1 score, respectively, of 0.92 and 0.89. These results show that c-VEP carries individual discriminative characteristics and it is feasible to develop a practical authentication system based on c-VEP.

RevDate: 2023-09-25

Chen X, Wang R, Khalilian-Gourtani A, et al (2023)

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis.

bioRxiv : the preprint server for biology pii:2023.09.16.558028.

Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies restoring speech function in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity, and high dimensionality, and the limited publicly available source code. Here, we present a novel deep learning-based neural speech decoding framework that includes an ECoG Decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable Speech Synthesizer that maps speech parameters to spectrograms. We develop a companion audio-to-audio auto-encoder consisting of a Speech Encoder and the same Speech Synthesizer to generate reference speech parameters to facilitate the ECoG Decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Among three neural network architectures for the ECoG Decoder, the 3D ResNet model has the best decoding performance (PCC=0.804) in predicting the original speech spectrogram, closely followed by the SWIN model (PCC=0.796). Our experimental results show that our models can decode speech with high correlation even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. We successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with speech deficits resulting from left hemisphere damage. Further, we use an occlusion analysis to identify cortical regions contributing to speech decoding across our models. Finally, we provide open-source code for our two-stage training pipeline along with associated preprocessing and visualization tools to enable reproducible research and drive research across the speech science and prostheses communities.

RevDate: 2023-09-26

Kuo CH, Tu TH, KT Chen (2023)

Editorial: Advanced technological applications in neurosurgery.

Frontiers in surgery, 10:1277997.

RevDate: 2023-09-27
CmpDate: 2023-09-25

Gao Z (2023)

Adenosine A2A receptor and glia.

International review of neurobiology, 170:29-48.

The adenosine A2A receptor (A2AR) is abundantly expressed in the brain, including both neurons and glial cells. While the expression of A2AR is relative low in glia, its levels elevate robustly in astrocytes and microglia under pathological conditions. Elevated A2AR appears to play a detrimental role in a number of disease states, by promoting neuroinflammation and astrocytic reaction to contribute to the progression of neurodegenerative and psychiatric diseases.

RevDate: 2023-09-23

Lee S, Kim H, Kim JB, et al (2023)

Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients.

Computers in biology and medicine, 166:107435 pii:S0010-4825(23)00900-9 [Epub ahead of print].

Motor imagery (MI)-based brain-computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and sensorimotor cortex during MI are similar to those of motor execution/imagery. However, individuals paralyzed owing to various neurological disorders have debilitated activation of the motor control region. Therefore, the differences in brain activation based on the paralysis location should be considered. We analyzed brain activation patterns using the electroencephalogram (EEG) acquired while performing MI on the right upper limb to investigate hemiplegia-related brain activation patterns. Participants with hemiplegia of the right upper limb (n=7) and left upper limb (n=4) performed the MI task within the right upper limb. EEG signals were acquired using 14 channels based on a 10-20 global system, and analyzed for event-related desynchronization (ERD) based on event-related spectral perturbation and functional connectivity, using the weighted phase-lag index of both hemispheres at the location of hemiplegia. Enhanced ERD was found in the ipsilateral region, compared to the contralateral region, after MI of the affected limb. The reduced difference in the centrality of the channels was observed in all subjects, likely reflecting an altered brain network from increased interhemispheric connections. Furthermore, the tendency of distinct network-based features depending on the MI task on the affected limb was diluted between the inter-hemispheres. Analysis of interaction between inter-region using functional connectivity could provide avenues for further investigation of BCI strategy through the brain state of individuals with hemiplegia.

RevDate: 2023-09-23

Li Z, Wang X, Xing Y, et al (2023)

Measuring multivariate phase synchronization with symbolization and permutation.

Neural networks : the official journal of the International Neural Network Society, 167:838-846 pii:S0893-6080(23)00365-9 [Epub ahead of print].

Phase synchronization is an important mechanism for the information processing of neurons in the brain. Most of the current phase synchronization measures are bivariate and focus on the synchronization between pairs of time series. However, these methods do not provide a full picture of global interactions in neural systems. Considering the prevalence and importance of multivariate neural signal analysis, there is an urgent need to quantify global phase synchronization (GPS) in neural networks. Therefore, we propose a new measure named symbolic phase difference and permutation entropy (SPDPE), which symbolizes the phase difference in multivariate neural signals and estimates GPS according to the permutation patterns of the symbolic sequences. The performance of SPDPE was evaluated using simulated data generated by Kuramoto and Rössler model. The results demonstrate that SPDPE exhibits low sensitivity to data length and outperforms existing methods in accurately characterizing GPS and effectively resisting noise. Moreover, to validate the method with real data, it was applied to classify seizures and non-seizures by calculating the GPS of stereoelectroencephalography (SEEG) data recorded from the onset zones of ten epilepsy patients. We believe that SPDPE will improve the estimation of GPS in many applications, such as EEG-based brain-computer interfaces, brain modeling, and simultaneous EEG-fMRI analysis.

RevDate: 2023-09-23

Zhou T, Kawasaki K, Suzuki T, et al (2023)

Mapping information flow between the inferotemporal and prefrontal cortices via neural oscillations in memory retrieval and maintenance.

Cell reports, 42(10):113169 pii:S2211-1247(23)01181-6 [Epub ahead of print].

Interaction between the inferotemporal (ITC) and prefrontal (PFC) cortices is critical for retrieving information from memory and maintaining it in working memory. Neural oscillations provide a mechanism for communication between brain regions. However, it remains unknown how information flow via neural oscillations is functionally organized in these cortices during these processes. In this study, we apply Granger causality analysis to electrocorticographic signals from both cortices of monkeys performing visual association tasks to map information flow. Our results reveal regions within the ITC where information flow to and from the PFC increases via specific frequency oscillations to form clusters during memory retrieval and maintenance. Theta-band information flow in both directions increases in similar regions in both cortices, suggesting reciprocal information exchange in those regions. These findings suggest that specific subregions function as nodes in the memory information-processing network between the ITC and the PFC.

RevDate: 2023-09-25
CmpDate: 2023-09-25

Wang H, Zhang X, Li J, et al (2023)

Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS.

Scientific reports, 13(1):15839.

For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain-computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers' mental activities in low-risk episode and high-risk episode were compared, the influences on passengers' mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers' driving risk cognition.

RevDate: 2023-09-22

Chen K, Garcia Padilla C, Kiselyov K, et al (2023)

Cell-specific alterations in autophagy-lysosomal activity near the chronically implanted microelectrodes.

Biomaterials, 302:122316 pii:S0142-9612(23)00324-1 [Epub ahead of print].

Intracortical microelectrodes that can record and stimulate brain activity have become a valuable technique for basic science research and clinical applications. However, long-term implantation of these microelectrodes can lead to progressive neurodegeneration in the surrounding microenvironment, characterized by elevation in disease-associated markers. Dysregulation of autophagy-lysosomal degradation, a major intracellular waste removal process, is considered a key factor in the onset and progression of neurodegenerative diseases. It is plausible that similar dysfunctions in autophagy-lysosomal degradation contribute to tissue degeneration following implantation-induced focal brain injury, ultimately impacting recording performance. To understand how the focal, persistent brain injury caused by long-term microelectrode implantation impairs autophagy-lysosomal pathway, we employed two-photon microscopy and immunohistology. This investigation focused on the spatiotemporal characterization of autophagy-lysosomal activity near the chronically implanted microelectrode. We observed an aberrant accumulation of immature autophagy vesicles near the microelectrode over the chronic implantation period. Additionally, we found deficits in autophagy-lysosomal clearance proximal to the chronic implant, which was associated with an accumulation of autophagy cargo and a reduction in lysosomal protease level during the chronic period. Furthermore, our evidence demonstrates reactive astrocytes have myelin-containing lysosomes near the microelectrode, suggesting its role of myelin engulfment during acute implantation period. Together, this study sheds light on the process of brain tissue degeneration caused by long-term microelectrode implantation, with a specific focus on impaired intracellular waste degradation.

RevDate: 2023-09-25
CmpDate: 2023-09-25

Abbasi A, Lassagne H, Estebanez L, et al (2023)

Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback.

Science advances, 9(38):eadh1328.

Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal spatiotemporal properties of the sensorimotor cortical integration that set constraints on the design of neuroprosthetics.

RevDate: 2023-09-25
CmpDate: 2023-09-25

Xin J, Shi Y, Zhang X, et al (2023)

Carotenoid assembly regulates quinone diffusion and the Roseiflexus castenholzii reaction center-light harvesting complex architecture.

eLife, 12:.

Carotenoid (Car) pigments perform central roles in photosynthesis-related light harvesting (LH), photoprotection, and assembly of functional pigment-protein complexes. However, the relationships between Car depletion in the LH, assembly of the prokaryotic reaction center (RC)-LH complex, and quinone exchange are not fully understood. Here, we analyzed native RC-LH (nRC-LH) and Car-depleted RC-LH (dRC-LH) complexes in Roseiflexus castenholzii, a chlorosome-less filamentous anoxygenic phototroph that forms the deepest branch of photosynthetic bacteria. Newly identified exterior Cars functioned with the bacteriochlorophyll B800 to block the proposed quinone channel between LHαβ subunits in the nRC-LH, forming a sealed LH ring that was disrupted by transmembrane helices from cytochrome c and subunit X to allow quinone shuttling. dRC-LH lacked subunit X, leading to an exposed LH ring with a larger opening, which together accelerated the quinone exchange rate. We also assigned amino acid sequences of subunit X and two hypothetical proteins Y and Z that functioned in forming the quinone channel and stabilizing the RC-LH interactions. This study reveals the structural basis by which Cars assembly regulates the architecture and quinone exchange of bacterial RC-LH complexes. These findings mark an important step forward in understanding the evolution and diversity of prokaryotic photosynthetic apparatus.

RevDate: 2023-09-23

Jovanovic LI, Jervis Rademeyer H, Pakosh M, et al (2023)

Scoping Review on Brain-Computer Interface-Controlled Electrical Stimulation Interventions for Upper Limb Rehabilitation in Adults: A Look at Participants, Interventions, and Technology.

Physiotherapy Canada. Physiotherapie Canada, 75(3):276-290.

PURPOSE: While current rehabilitation practice for improving arm and hand function relies on physical/occupational therapy, a growing body of research evaluates the effects of technology-enhanced rehabilitation. We review interventions that combine a brain-computer interface (BCI) with electrical stimulation (ES) for upper limb movement rehabilitation to summarize the evidence on (1) populations of study participants, (2) BCI-ES interventions, and (3) the BCI-ES systems.

METHOD: After searching seven databases, two reviewers identified 23 eligible studies. We consolidated information on the study participants, interventions, and approaches used to develop integrated BCI-ES systems. The included studies investigated the use of BCI-ES interventions with stroke and spinal cord injury (SCI) populations. All studies used electroencephalography to collect brain signals for the BCI, and functional electrical stimulation was the most common type of ES. The BCI-ES interventions were typically conducted without a therapist, with sessions varying in both frequency and duration.

RESULTS: Of the 23 eligible studies, only 3 studies involved the SCI population, compared to 20 involving individuals with stroke.

CONCLUSIONS: Future BCI-ES interventional studies could address this gap. Additionally, standardization of device and rehabilitation modalities, and study-appropriate involvement with therapists, can be considered to advance this intervention towards clinical implementation.

RevDate: 2023-09-23

Boscolo Galazzo I, Tonin L, Miladinović A, et al (2023)

Editorial: Brain-connectivity-based computer interfaces.

Frontiers in human neuroscience, 17:1281446.

RevDate: 2023-09-23

Mirfathollahi A, Ghodrati MT, Shalchyan V, et al (2023)

Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement.

iScience, 26(10):107808.

Area 2 of the primary somatosensory cortex (S1), encodes proprioceptive information of limbs. Several studies investigated the encoding of movement parameters in this area. However, the single-trial decoding of these parameters, which can provide additional knowledge about the amount of information available in sub-regions of this area about instantaneous limb movement, has not been well investigated. We decoded kinematic and kinetic parameters of active and passive hand movement during center-out task using conventional and state-based decoders. Our results show that this area can be used to accurately decode position, velocity, force, moment, and joint angles of hand. Kinematics had higher accuracies compared to kinetics and active trials were decoded more accurately than passive trials. Although the state-based decoder outperformed the conventional decoder in the active task, it was the opposite in the passive task. These results can be used in intracortical micro-stimulation procedures to provide proprioceptive feedback to BCI subjects.

RevDate: 2023-09-21

Liang S, Zhao L, Ni P, et al (2023)

Frontostriatal circuitry and the tryptophan kynurenine pathway in major psychiatric disorders.

Psychopharmacology [Epub ahead of print].

RATIONALE: An imbalance of the tryptophan kynurenine pathway (KP) commonly occurs in psychiatric disorders, though the neurocognitive and network-level effects of this aberration are unclear.

OBJECTIVES: In this study, we examined the connection between dysfunction in the frontostriatal brain circuits, imbalances in the tryptophan kynurenine pathway (KP), and neurocognition in major psychiatric disorders.

METHODS: Forty first-episode medication-naive patients with schizophrenia (SCZ), fifty patients with bipolar disorder (BD), fifty patients with major depressive disorder (MDD), and forty-two healthy controls underwent resting-state functional magnetic resonance imaging. Plasma levels of KP metabolites were measured, and neurocognitive function was evaluated. Frontostriatal connectivity and KP metabolites were compared between groups while controlling for demographic and clinical characteristics. Canonical correlation analyses were conducted to explore multidimensional relationships between frontostriatal circuits-KP and KP-cognitive features.

RESULTS: Patient groups shared hypoconnectivity between bilateral ventrolateral prefrontal cortex (vlPFC) and left insula, with disorder-specific dysconnectivity in SCZ related to PFC, left dorsal striatum hypoconnectivity. The BD group had higher anthranilic acid and lower xanthurenic acid levels than the other groups. KP metabolites and ratios related to disrupted frontostriatal dysconnectivity in a transdiagnostic manner. The SCZ group and MDD group separately had high-dimensional associations between KP metabolites and cognitive measures.

CONCLUSIONS: The findings suggest that KP may influence cognitive performance across psychiatric conditions via frontostriatal dysfunction.

RevDate: 2023-09-21

Prinsloo S, Kaptchuk TJ, De Ridder D, et al (2023)

Brain-computer interface relieves chronic chemotherapy-induced peripheral neuropathy: A randomized, double-blind, placebo-controlled trial.

Cancer [Epub ahead of print].

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) includes negative sensations that remain a major chronic problem for cancer survivors. Previous research demonstrated that neurofeedback (a closed-loop brain-computer interface [BCI]) was effective at treating CIPN versus a waitlist control (WLC). The authors' a priori hypothesis was that BCI would be superior to placebo feedback (placebo control [PLC]) and to WLC in alleviating CIPN and that changes in brain activity would predict symptom report.

METHODS: Randomization to one of three conditions occurred between November 2014 and November 2018. Breast cancer survivors no longer in treatment were assessed at baseline, at the end of 20 treatment sessions, and 1 month later. Auditory and visual rewards were given over 20 sessions based on each patient's ability to modify their own electroencephalographic signals. The Pain Quality Assessment Scale (PQAS) at the end of treatment was the primary outcome, and changes in electroencephalographic signals and 1-month data also were examined.

RESULTS: The BCI and PLC groups reported significant symptom reduction. The BCI group demonstrated larger effect size differences from the WLC group than the PLC group (mean change score: BCI vs. WLC, -2.60 vs. 0.38; 95% confidence interval, -3.67, -1.46 [p = .000; effect size, 1.07]; PLC, -2.26; 95% confidence interval, -3.33, -1.19 [p = .001 vs. WLC; effect size, 0.9]). At 1 month, symptoms continued to improve only for the BCI group. Targeted brain changes at the end of treatment predicted symptoms at 1 month for the BCI group only.

CONCLUSIONS: BCI is a promising treatment for CIPN and may have a longer lasting effect than placebo (nonspecific BCI), which is an important consideration for long-term symptom relief. Although scientifically interesting, the ability to separate real from placebo treatment may not be as important as understanding the placebo effects differently from effects of the intervention.

PLAIN LANGUAGE SUMMARY: Chemotherapy-induced nerve pain (neuropathy) can be disabling for cancer survivors; however, the way symptoms are felt depends on how the brain interprets the signals from nerves in the body. We determined that the perception of neuropathy can be changed by working directly with the brain. Survivors in our trial played 20 sessions of a type of video game that was designed to change the way the brain processed sensation and movement. In this, our second trial, we again observed significant improvement in symptoms that lasted after the treatment was complete.

RevDate: 2023-09-22

Li F, Zhang D, Chen J, et al (2023)

Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis.

Frontiers in neuroscience, 17:1243151.

BACKGROUND: The incidence and mortality rates of stroke are escalating due to the growing aging population, which presents a significant hazard to human health. In the realm of stroke, brain-computer interface (BCI) technology has gained considerable attention as a means to enhance treatment efficacy and improve quality of life. Consequently, a bibliometric visualization analysis was performed to investigate the research hotspots and trends of BCI technology in stroke, with the objective of furnishing reference and guidance for future research.

METHODS: This study utilized the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as the data source, selecting relevant literature published between 2013 and 2022 as research sample. Through the application of VOSviewer 1.6.19 and CiteSpace 6.2.R2 visualization analysis software, as well as the bibliometric online analysis platform, the scientific knowledge maps were constructed and subjected to visualization display, and statistical analysis.

RESULTS: This study encompasses a total of 693 relevant literature, which were published by 2,556 scholars from 975 institutions across 53 countries/regions and have been collected by 185 journals. In the past decade, BCI technology in stroke research has exhibited an upward trend in both annual publications and citations. China and the United States are high productivity countries, while the University of Tubingen stands out as the most contributing institution. Birbaumer N and Pfurtscheller G are the authors with the highest publication and citation frequency in this field, respectively. Frontiers in Neuroscience has published the most literature, while Journal of Neural Engineering has the highest citation frequency. The research hotspots in this field cover keywords such as stroke, BCI, rehabilitation, motor imagery (MI), motor recovery, electroencephalogram (EEG), neurorehabilitation, neural plasticity, task analysis, functional electrical stimulation (FES), motor impairment, feature extraction, and induced movement therapy, which to a certain extent reflect the development trend and frontier research direction of this field.

CONCLUSION: This study comprehensively and visually presents the extensive and in-depth literature resources of BCI technology in stroke research in the form of knowledge maps, which facilitates scholars to gain a more convenient understanding of the development and prospects in this field, thereby promoting further research work.

RevDate: 2023-09-21

Kosnoff J, Yu K, Liu C, et al (2023)

Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention.

bioRxiv : the preprint server for biology pii:2023.09.04.556252.

Paralysis affects roughly 1 in 50 Americans. While there is no cure for the condition, brain-computer interfaces (BCI) can allow users to control a device with their mind, bypassing the paralyzed region. Non-invasive BCIs still have high error rates, which is hypothesized to be reduced with concurrent targeted neuromodulation. This study examines whether transcranial focused ultrasound (tFUS) modulation can improve BCI outcomes, and what the underlying mechanism of action might be through high-density electroencephalography (EEG)-based source imaging (ESI) analyses. V5-targeted tFUS significantly reduced the error for the BCI speller task. ESI analyses showed significantly increased theta activity in the tFUS condition at both V5 and downstream the dorsal visual processing pathway. Correlation analysis indicates that the dorsal processing pathway connection was preserved during tFUS stimulation, whereas extraneous connections were severed. These results suggest that V5-targeted tFUS' mechanism of action is to raise the brain's feature-based attention to visual motion.

RevDate: 2023-09-23
CmpDate: 2023-09-22

Ozcelik F, R VanRullen (2023)

Natural scene reconstruction from fMRI signals using generative latent diffusion.

Scientific reports, 13(1):15666.

In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called "Brain-Diffuser". In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. On the publicly available Natural Scenes Dataset benchmark, our method outperforms previous models both qualitatively and quantitatively. When applied to synthetic fMRI patterns generated from individual ROI (region-of-interest) masks, our trained model creates compelling "ROI-optimal" scenes consistent with neuroscientific knowledge. Thus, the proposed methodology can have an impact on both applied (e.g. brain-computer interface) and fundamental neuroscience.

RevDate: 2023-09-20

Wang W, Li B, Wang H, et al (2023)

EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification.

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

Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising paradigm for brain-computer interface (BCI) systems and has been extensively employed in various BCI applications, including assisting disabled individuals, controlling devices and environments, and enhancing human capabilities. The high-performance decoding capability of MI-EEG signals is a key issue that impacts the development of the industry. However, decoding MI-EEG signals is challenging due to the low signal-to-noise ratio and inter-subject variability. In response to the aforementioned core problems, this paper proposes a novel end-to-end network, a fusion multi-branch 1D convolutional neural network (EEG-FMCNN), to decode MI-EEG signals without pre-processing. The utilization of multi-branch 1D convolution not only exhibits a certain level of noise tolerance but also addresses the issue of inter-subject variability to some extent. This is attributed to the ability of multi-branch architectures to capture information from different frequency bands, enabling the establishment of optimal convolutional scales and depths. Furthermore, we incorporate 1D squeeze-and-excitation (SE) blocks and shortcut connections at appropriate locations to further enhance the generalization and robustness of the network. In the BCI Competition IV-2a dataset, our proposed model has obtained good experimental results, achieving accuracies of 78.82% and 68.41% for subject-dependent and subject-independent modes, respectively. In addition, extensive ablative experiments and fine-tuning experiments were conducted, resulting in a notable 7% improvement in the average performance of the network, which holds significant implications for the generalization and application of the network.

RevDate: 2023-09-21

Asanza V, Lorente-Leyva LL, Peluffo-Ordóñez DH, et al (2023)

MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks.

Data in brief, 50:109540.

Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.

RevDate: 2023-09-19

Ju C, C Guan (2023)

Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis.

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

The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces (BCIs) based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In this study, we amplify the geometric deep-learning-based MI-EEG classifiers from the perspective of time-frequency analysis, introducing a new architecture called Graph-CSPNet. We refer to this category of classifiers as Geometric Classifiers, highlighting their foundation in differential geometry stemming from EEG spatial covariance matrices. Graph-CSPNet utilizes novel manifold-valued graph convolutional techniques to capture the EEG features in the time-frequency domain, offering heightened flexibility in signal segmentation for capturing localized fluctuations. To evaluate the effectiveness of Graph-CSPNet, we employ five commonly used publicly available MI-EEG datasets, achieving near-optimal classification accuracies in nine out of 11 scenarios. The Python repository can be found at

RevDate: 2023-09-19

Yang S, Wang H, Pang Y, et al (2023)

NADOL: Neuromorphic Architecture for Spike-driven Online Learning By Dendrites.

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

Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essential solution towards spike-based machine intelligence and neural learning systems. However, on-line learning capability for neuromorphic models is still an open challenge. In this study a novel neuromorphic architecture with dendritic on-line learning (NADOL) is presented, which is a novel efficient methodology for brain-inspired intelligence on embedded hardware. With the feature of distributed processing using spiking neural network, NADOL can cut down the power consumption and enhance the learning efficiency and convergence speed. A detailed analysis for NADOL is presented, which demonstrates the effects of different conditions on learning capabilities, including neuron number in hidden layer, dendritic segregation parameters, feedback connection, and connection sparseness with various levels of amplification. Piecewise linear approximation approach is used to cut down the computational resource cost. The experimental results demonstrate a remarkable learning capability that surpasses other solutions, with NADOL exhibiting superior performance over the GPU platform in dendritic learning. This study's applicability extends across diverse domains, including the Internet of Things, robotic control, and brain-machine interfaces. Moreover, it signifies a pivotal step in bridging the gap between artificial intelligence and neuroscience through the introduction of an innovative neuromorphic paradigm.

RevDate: 2023-09-20

Ayoub M, Ballout AA, Zayek RA, et al (2023)

Mind + Machine: ChatGPT as a Basic Clinical Decisions Support Tool.

Cureus, 15(8):e43690.

Background Generative artificial intelligence (AI) has integrated into various industries as it has demonstrated enormous potential in automating elaborate processes and enhancing complex decision-making. The ability of these chatbots to critically triage, diagnose, and manage complex medical conditions, remains unknown and requires further research. Objective This cross-sectional study sought to quantitatively analyze the appropriateness of ChatGPT (OpenAI, San Francisco, CA, US) in its ability to triage, synthesize differential diagnoses, and generate treatment plans for nine diverse but common clinical scenarios. Methods Various common clinical scenarios were developed. Each was input into ChatGPT, and the chatbot was asked to develop diagnostic and treatment plans. Five practicing physicians independently scored ChatGPT's responses to the clinical scenarios. Results The average overall score for the triage ranking was 4.2 (SD 0.7). The lowest overall score was for the completeness of the differential diagnosis at 4.1 (0.5). The highest overall scores were seen with the accuracy of the differential diagnosis, initial treatment plan, and overall usefulness of the response (all with an average score of 4.4). Variance among physician scores ranged from 0.24 for accuracy of the differential diagnosis to 0.49 for appropriateness of triage ranking. Discussion ChatGPT has the potential to augment clinical decision-making. More extensive research, however, is needed to ensure accuracy and appropriate recommendations are provided.

RevDate: 2023-09-22
CmpDate: 2023-09-20

Khanal S, Miani C, Finne E, et al (2023)

Effectiveness of behavior change interventions for smoking cessation among expectant and new fathers: findings from a systematic review.

BMC public health, 23(1):1812.

BACKGROUND: Smoking cessation during pregnancy and the postpartum period by both women and their partners offers multiple health benefits. However, compared to pregnant/postpartum women, their partners are less likely to actively seek smoking cessation services. There is an increased recognition about the importance of tailored approaches to smoking cessation for expectant and new fathers. While Behavior Change Interventions (BCIs) are a promising approach for smoking cessation interventions, evidence on effectiveness exclusively among expectant and new fathers are fragmented and does not allow for many firm conclusions to be drawn.

METHODS: We conducted a systematic review on effectiveness of BCIs on smoking cessation outcomes of expectant and new fathers both through individual and/or couple-based interventions. Peer reviewed articles were identified from eight databases without any date or language restriction.Two independent reviewers screened studies for relevance, assessed methodological quality of relevant studies, and extracted data from studies using a predeveloped data extraction sheet.

RESULTS: We retrieved 1222 studies, of which 39 were considered for full text screening after reviewing the titles and abstracts. An additional eight studies were identified from reviewing the reference list of review articles picked up by the databases search. A total of nine Randomised Control Trials were included in the study. Six studies targeted expectant/new fathers, two targeted couples and one primarily targeted women with an intervention component to men. While the follow-up measurements for men varied across studies, the majority reported biochemically verified quit rates at 6 months. Most of the interventions showed positive effects on cessation outcomes. BCI were heterogenous across studies. Findings are suggestive of gender targeted interventions being more likely to have positive cessation outcomes.

CONCLUSIONS: This systematic review found limited evidence supporting the effectiveness of BCI among expectant and new fathers, although the majority of studies show positive effects of these interventions on smoking cessation outcomes. There remains a need for more research targeted at expectant and new fathers. Further, there is a need to identify how smoking cessation service delivery can better address the needs of (all) gender(s) during pregnancy.

RevDate: 2023-09-22

Tang J, Xi X, Wang T, et al (2023)

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient.

Journal of neural engineering, 20(5):.

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system.Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model.Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals' local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices.Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

RevDate: 2023-09-18

Guo Y, Sun L, Zhong W, et al (2024)

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges.

Neural regeneration research, 19(3):663-670.

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury. Specifically, it can be used to analyze and process data regarding peripheral nerve injury and repair, while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms. To investigate advances in the use of artificial intelligence in the diagnosis, rehabilitation, and scientific examination of peripheral nerve injury, we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994-2023. We identified the following research hotspots in peripheral nerve injury and repair: (1) diagnosis, classification, and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques, such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy; (2) motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms, such as wearable devices and assisted wheelchair systems; (3) improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning, such as implantable peripheral nerve interfaces; (4) the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility, enabling them to control devices such as networked hand prostheses; (5) artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation, thereby reducing surgical risk and complications, and facilitating postoperative recovery. Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair, there are some limitations to this technology, such as the consequences of missing or imbalanced data, low data accuracy and reproducibility, and ethical issues (e.g., privacy, data security, research transparency). Future research should address the issue of data collection, as large-scale, high-quality clinical datasets are required to establish effective artificial intelligence models. Multimodal data processing is also necessary, along with interdisciplinary collaboration, medical-industrial integration, and multicenter, large-sample clinical studies.

RevDate: 2023-09-19

Ding Y, Guo K, Wang X, et al (2023)

Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy.

Translational neuroscience, 14(1):20220308.

OBJECTIVE: This study aims to investigate the impact of vagus nerve stimulation (VNS) on the connectivity and small-world metrics of brain functional networks during seizure periods.

METHODS: Ten refractory epilepsy patients underwent video encephalographic monitoring before and after VNS treatment. The 2-min electroencephalogram segment containing the ictal was selected for each participant, resulting in a total of 20 min of seizure data. The weighted phase lag index (wPLI) and small-world metrics were calculated for the whole frequency band and different frequency bands (delta, theta, alpha, beta, and gamma). Finally, the relevant metrics were statistically analyzed, and the false discovery rate was used to correct for differences after multiple comparisons.

RESULTS: In the whole band, the wPLI was notably enhanced, and the network metrics, including degree (D), clustering coefficient (CC), and global efficiency (GE), increased, while characteristic path length (CPL) decreased (P < 0.01). In different frequency bands, the wPLI between the parieto-occipital and frontal regions was significantly strengthened in the delta and beta bands, while the wPLI within the frontal region and between the frontal and parieto-occipital regions were significantly reduced in the beta and gamma bands (P < 0.01). In the low-frequency band (<13 Hz), the small-world metrics demonstrated significantly increased CC, D, and GE, with a significantly decreased CPL, indicating a more efficient network organization. In contrast, in the gamma band, the GE decreased, and the CPL increased, suggesting a shift toward less efficient network organization.

CONCLUSION: VNS treatment can significantly change the wPLI and small-world metrics. These findings contribute to a deeper understanding of the impact of VNS therapy on brain networks and provide objective indicators for evaluating the efficacy of VNS.

RevDate: 2023-09-17

Mahemuti Y, Kadeer K, Su R, et al (2023)

TSPO exacerbates acute cerebral ischemia/reperfusion injury by inducing autophagy dysfunction.

Experimental neurology pii:S0014-4886(23)00227-3 [Epub ahead of print].

Autophagy is considered a double-edged sword, with a role in the regulation of the pathophysiological processes of the central nervous system (CNS) after cerebral ischemia-reperfusion injury (CIRI). The 18-kDa translocator protein (TSPO) is a highly conserved protein, with its expression level in the nervous system closely associated with the regulation of pathophysiological processes. In addition, the ligand of TSPO reduces neuroinflammation in brain diseases, but the potential role of TSPO in CIRI is largely undiscovered. On this basis, we investigated whether TSPO regulates neuroinflammatory response by affecting autophagy in microglia. In our study, increased expression of TSPO was detected in rat brain tissues with transient middle cerebral artery occlusion (tMCAO) and in BV2 microglial cells exposed to oxygen-glucose deprivation or reoxygenation (OGD/R) treatment, respectively. In addition, we confirmed that autophagy was over-activated during CIRI by increased expression of autophagy activation related proteins with Beclin-1 and LC3B, while the expression of p62 was decreased. The degradation process of autophagy was inhibited, while the expression levels of LAMP-1 and Cathepsin-D were significantly reduced. Results of confocal laser microscopy and transmission electron microscopy (TEM) indicated that autophagy flux was disordered. In contrast, inhibition of TSPO prevented autophagy over-activation both in vivo and in vitro. Interestingly, suppression of TSPO alleviated nerve cell damage by reducing reactive oxygen species (ROS) and pro-inflammatory factors, including TNF-α and IL-6 in microglia cells. In summary, these results indicated that TSPO might affect CIRI by mediating autophagy dysfunction and thus might serve as a potential target for ischemic stroke treatment.

RevDate: 2023-09-17

Yang X, Zhu HR, Tao YJ, et al (2023)

Multivariate classification based on large-scale brain networks during early abstinence predicted lapse among male detoxified alcohol-dependent patients.

Asian journal of psychiatry, 89:103767 pii:S1876-2018(23)00323-4 [Epub ahead of print].

Identifying biomarkers to predict lapse of alcohol-dependence (AD) is essential for treatment and prevention strategies, but remains remarkably challenging. With an aim to identify neuroimaging features for predicting AD lapse, 66 male AD patients during early-abstinence (baseline) after hospitalized detoxification underwent resting-state functional magnetic resonance imaging and were then followed-up for 6 months. The relevance-vector-machine (RVM) analysis on baseline large-scale brain networks yielded an elegant model for differentiating relapsing patients (n = 38) from abstainers, with the area under the curve of 0.912 and the accuracy by leave-one-out cross-validation of 0.833. This model captured key information about neuro-connectome biomarkers for predicting AD lapse.

RevDate: 2023-09-16

Fan J, H Xu (2023)

Serotonin: A Bridge for Infant-mother Bonding.

Neuroscience bulletin [Epub ahead of print].

RevDate: 2023-09-15

Tian L, Zhao T, Dong L, et al (2023)

Passive array micro-magnetic stimulation device based on multi-carrier wireless flexible control for magnetic neuromodulation.

Journal of neural engineering [Epub ahead of print].

The passive micro-magnetic stimulation (µMS) devices typically consist of an external transmitting coil and a single internal micro-coil, which enables a point-to-point energy supply from the external coil to the internal coil and the realization of magnetic neuromodulation via wireless energy transmission. The internal array of micro coils can achieve multi-target stimulation without movement, which improves the focus and effectiveness of magnetic stimulations. However, achieving a free selection of an appropriate external coil to deliver energy to a particular internal array of micro-coils for multiple stimulation targets has been challenging. To address this challenge, this study uses a multi-carrier modulation technique to transmit the energy of the external coil. Approach: In this study, a theoretical model of a multi-carrier resonant compensation network for the array µMS is established based on the principle of magnetically coupled resonance. The resonant frequency coupling parameter corresponding to each micro-coil of the array µMS is determined, and the magnetic field interference between the external coil and its non-resonant micro-coils is eliminated. Therefore, an effective magnetic stimulation threshold for a micro-coil corresponding to the target is determined, and wireless free control of the internal micro-coil array is achieved by using an external transmitting coil. Main results: The passive µMS array model is designed using a multi-carrier wireless modulation method, and its synergistic modulation of the magnetic stimulation of synaptic plasticity Long-term Potentiation in multiple hippocampal regions is investigated using hippocampal isolated brain slices. Significance: The results presented in this study could provide theoretical and experimental bases for implantable micro-magnetic device-targeted therapy, introducing an efficient method for diagnosis and treatment of neurological diseases and providing innovative ideas for in-depth application of micro-magnetic stimulation in the neuroscience field. .

RevDate: 2023-09-15

Fadli RAA, Yamanouchi Y, Jovanovic LI, et al (2023)

Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation.

Journal of neural engineering [Epub ahead of print].

Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) could excite the central nervous system to enhance upper limb motor recovery. Our current study assessed the effectiveness of motor and prefrontal cortical activity-based BCI-FES to help elucidate the underlying neuromodulation mechanisms of this neurorehabilitation approach. Approach: The primary motor cortex (M1) and prefrontal cortex (PFC) BCI-FES interventions were performed for 25 min on separate days with twelve non-disabled participants. During the interventions, a single electrode from the contralateral M1 or PFC was used to detect event-related desynchronization (ERD) in the calibrated frequency range. If the BCI system detected ERD within 15 s of motor imagery, FES activated wrist extensor muscles. Otherwise, if the BCI system did not detect ERD within 15 s, a subsequent trial was initiated without FES. To evaluate neuromodulation effects, corticospinal excitability was assessed using single-pulse transcranial magnetic stimulation, and cortical excitability was assessed by motor imagery ERD and resting-state functional connectivity before, immediately, 30 min, and 60 min after each intervention. Main results: M1 and PFC BCI-FES interventions had similar success rates of approximately 80%, while the M1 intervention was faster in detecting ERD activity. Consequently, only the M1 intervention effectively elicited corticospinal excitability changes for at least 60 min around the targeted cortical area in the M1, suggesting a degree of spatial localization. However, cortical excitability measures did not indicate changes after either M1 or PFC BCI-FES. Significance: Neural mechanisms underlying the effectiveness of BCI-FES neuromodulation may be attributed to the M1 direct corticospinal projections and/or the closer timing between ERD detection and FES, which likely enhanced Hebbian-like plasticity by synchronizing cortical activation detected by the BCI system with the sensory nerve activation and movement related reafference elicited by FES.

RevDate: 2023-09-18

Ma R, Chen YF, Jiang YC, et al (2023)

A New Compound-limbs Paradigm: Integrating upper-limb swing improves lower-limb stepping intention decoding from EEG.

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) systems based on spontaneous electroencephalography (EEG) hold the promise to implement human voluntary control of lower-extremity powered exoskeletons. However, current EEG-BCI paradigms do not consider the cooperation of upper and lower limbs during walking, which is inconsistent with natural human stepping patterns. To deal with this problem, this study proposed a stepping-matched human EEG-BCI paradigm that involved actions of both unilateral lower and contralateral upper limbs (also referred to as compound-limbs movement). Experiments were conducted in motor execution (ME) and motor imagery (MI) conditions to validate the feasibility. Common spatial pattern (CSP) proposed subject-specific CSP (SSCSP), and filter-bank CSP (FBCSP) methods were applied for feature extraction, respectively. The best average classification results based on SSCSP indicated that the accuracies of compound-limbs paradigms in ME and MI conditions achieved 89.02% ± 12.84% and 73.70% ± 12.47%, respectively. Although they were 2.03% and 5.68% lower than those of the single-upper-limb mode that does not match human stepping patterns, they were 24.30% and 11.02% higher than those of the single-lower-limb mode. These findings indicated that the proposed compound-limbs EEG-BCI paradigm is feasible for decoding human stepping intention and thus provides a potential way for natural human control of walking assistance devices.

RevDate: 2023-09-17

Alonso-Valerdi LM (2023)

Editorial: Improving decoding of neuroinformation: towards the diversity of neural engineering applications.

Frontiers in human neuroscience, 17:1270696.

RevDate: 2023-09-18
CmpDate: 2023-09-18

Anonymous (2023)

Erratum: Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke.

Journal of visualized experiments : JoVE.

An erratum was issued for: Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke. The Authors section was updated from: Yongchun Jiang[1,2,3] Junxiao Yin[4] Biyi Zhao[1,3,5] Yajie Zhang[1,3] Tingting Peng[1,3] Wanqi Zhuang[1,3] Siqing Wang[1,3] Siqi Huang[1,3] Meilian Zhong[1,2,3] Yanni Zhang[1,3] Guibing Tang[1,3] Bingchi Shen[6] Haining Ou[1,3] Yuxin Zheng[2,3] Qiang Lin[2,3] [1]Guangzhou Medical University [2]Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University [3]Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University [4]Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine [5]School of Traditional Chinese Medicine, Jinan University [6]Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University to: Yongchun Jiang[1,2,3] Junxiao Yin[4] Biyi Zhao[1,3,5] Yajie Zhang[1,3] Tingting Peng[1,3] Wanqi Zhuang[1,3] Siqing Wang[1,3] Siqi Huang[1,3] Meilian Zhong[1,3] Yanni Zhang[1,3] Guibing Tang[1,3] Bingchi Shen[6] Haining Ou[1,3] Yuxin Zheng[2,3] Qiang Lin[2,3] [1]Guangzhou Medical University [2]Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University [3]Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University [4]Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine [5]School of Traditional Chinese Medicine, Jinan University [6]Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University.

RevDate: 2023-09-14

Azizi H, Fakhari A, Farahbakhsh M, et al (2023)

Prevention of Re-attempt Suicide Through Brief Contact Interventions: A Systematic Review, Meta-analysis, and Meta-regression of Randomized Controlled Trials.

Journal of prevention (2022) [Epub ahead of print].

Brief contact intervention (BCI) is a low-cost intervention to prevent re-attempt suicide. This meta-analysis and meta-regression study aimed to evaluate the effect of BCI on re-attempt prevention following suicide attempts (SAs). We systematically searched using defined keywords in MEDLINE, Embase, and Scopus up to April, 2023. All randomized controlled trials (RCTs) were eligible for inclusion after quality assessment. Random-effects model and subgroup analysis were used to estimate pooled risk difference (RD) and risk ratio (RR) between BCI and re-attempt prevention with 95% confidence intervals (CIs). Meta-regression analysis was carried out to explore the potential sources of heterogeneity. The pooled estimates were (RD = 4%; 95% CI 2-6%); and (RR = 0.62; 95% CI 0.48-0.77). Subgroup analysis demonstrated that more than 12 months intervention (RR = 0.46; 95% CI 0.10-0.82) versus 12 months or less (RR = 0.67; 95% CI 0.54-0.80) increased the effectiveness of BCI on re-attempt suicide reduction. Meta-regression analysis explored that BCI time (more than 12 months), BCI type, age, and female sex were the potential sources of the heterogeneity. The meta-analysis indicated that BCI could be a valuable strategy to prevent suicide re-attempts. BCI could be utilized within suicide prevention strategies as a surveillance component of mental health since BCI requires low-cost and low-educated healthcare providers.

RevDate: 2023-09-14

Horner S, Burleigh L, Traylor Z, et al (2023)

Looking on the bright side: the impact of ambivalent images on emotion regulation choice.

Cognition & emotion [Epub ahead of print].

Previous research has found that people choose to reappraise low intensity images more often than high intensity images. However, this research does not account for image ambivalence, which is presence of both positive and negative cues in a stimulus. The purpose of this research was to determine differences in ambivalence in high intensity and low intensity images used in previous research (experiments 1-2), and if ambivalence played a role in emotion regulation choice in addition to intensity (experiments 3-4). Experiments 1 and 2 found that the low intensity images were more ambivalent than the high intensity images. Experiment 2 further found a positive relationship between ambivalence of an image and reappraisal affordances. Experiments 3 and 4 found that people chose to reappraise ambivalent images more often than non-ambivalent images, and they also chose to reappraise low intensity images more often than high intensity images. These experiments support the idea that ambivalence is a factor in emotion regulation choice. Future research should consider the impact ambivalent stimuli have on emotion regulation, including the potential for leveraging ambivalent stimuli to improve one's emotion regulation ability.

RevDate: 2023-09-16

Liu C, You J, Wang K, et al (2023)

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.

Frontiers in neuroscience, 17:1180471.

OBJECTIVE: In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement.

APPROACH: Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm.

MAIN RESULTS: As a result, both the MRCP and ERD features showed the specific temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively.

SIGNIFICANCE: This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

RevDate: 2023-09-15

Fan C, Yang B, Li X, et al (2023)

Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.

Frontiers in neuroscience, 17:1250991.

Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.

RevDate: 2023-09-12

Feng X, Feng X, Qin B, et al (2023)

Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation.

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

Electroencephalography-to-Text generation (EEG-to-Text), which aims to directly generate natural text from EEG signals has drawn increasing attention in recent years due to the enormous potential for Brain-computer interfaces. However, the remarkable discrepancy between the subject-dependent EEG representation and the semantic-dependent text representation poses a great challenge to this task. To mitigate this, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C-SCL), which effectively recalibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thereby reducing the discrepancy. Specifically, our C-SCL pulls semantically similar EEG representations together while pushing apart dissimilar ones. Besides, in order to introduce more meaningful contrastive pairs, we carefully employ curriculum learning to not only craft meaningful contrastive pairs but also make the learning progressively. We conduct extensive experiments on the ZuCo benchmark and our method combined with diverse models and architectures shows stable improvements across three types of metrics while achieving the new state-of-the-art. Further investigation proves not only its superiority in both the single-subject and low-resource settings but also its robust generalizability in the zero-shot setting.

RevDate: 2023-09-11

Marín-Medina DS, Arenas-Vargas PA, Arias-Botero JC, et al (2023)

New approaches to recovery after stroke.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology [Epub ahead of print].

After a stroke, several mechanisms of neural plasticity can be activated, which may lead to significant recovery. Rehabilitation therapies aim to restore surviving tissue over time and reorganize neural connections. With more patients surviving stroke with varying degrees of neurological impairment, new technologies have emerged as a promising option for better functional outcomes. This review explores restorative therapies based on brain-computer interfaces, robot-assisted and virtual reality, brain stimulation, and cell therapies. Brain-computer interfaces allow for the translation of brain signals into motor patterns. Robot-assisted and virtual reality therapies provide interactive interfaces that simulate real-life situations and physical support to compensate for lost motor function. Brain stimulation can modify the electrical activity of neurons in the affected cortex. Cell therapy may promote regeneration in damaged brain tissue. Taken together, these new approaches could substantially benefit specific deficits such as arm-motor control and cognitive impairment after stroke, and even the chronic phase of recovery, where traditional rehabilitation methods may be limited, and the window for repair is narrow.

RevDate: 2023-09-11

Lu K, Y Pan (2023)

A collective neuroscience lens on intergroup conflict.

Trends in cognitive sciences pii:S1364-6613(23)00229-2 [Epub ahead of print].

How do team leaders and followers synchronize their behaviors and brains to effectively manage intergroup conflicts? Zhang and colleagues offered a collective neurobehavioral narrative that delves into the intricacies of intergroup conflict. Their results underscore the importance of leaders' group-oriented actions, along with leader-follower synchronization, in intergroup conflict resolution.

RevDate: 2023-09-11

Pitt KM, Cole ZJ, J Zosky (2023)

Promoting Simple and Engaging Brain-Computer Interface Designs for Children by Evaluating Contrasting Motion Techniques.

Journal of speech, language, and hearing research : JSLHR [Epub ahead of print].

PURPOSE: There is an increasing focus on using motion in augmentative and alternative communication (AAC) systems. In considering brain-computer interface access to AAC (BCI-AAC), motion may provide a simpler or more intuitive avenue for BCI-AAC control. Different motion techniques may be utilized in supporting competency with AAC devices including simple (e.g., zoom) and complex (behaviorally relevant animation) methods. However, how different pictorial symbol animation techniques impact BCI-AAC is unclear.

METHOD: Sixteen healthy children completed two experimental conditions. These conditions included highlighting of pictorial symbols via both functional (complex) and zoom (simple) animation to evaluate the effects of motion techniques on P300-based BCI-AAC signals and offline (predicted) BCI-AAC performance.

RESULTS: Functional (complex) animation significantly increased attentional-related P200/P300 event-related potential (ERP) amplitudes in the parieto-occipital area. Zoom (simple) animation significantly decreased N400 latency. N400 ERP amplitude was significantly greater, and occurred significantly earlier, on the right versus left side for the functional animation condition within the parieto-occipital bin. N200 ERP latency was significantly reduced over the left hemisphere for the zoom condition in the central bin. As hypothesized, elicitation of all targeted ERP components supported offline (predicted) BCI-AAC performance being similar between conditions.

CONCLUSION: Study findings provide continued support for the use of animation in BCI-AAC systems for children and highlight differences in neural and attentional processing between complex and simple animation techniques.


RevDate: 2023-09-11

Rustamov N, Souders L, Sheehan L, et al (2023)

IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Recovery in Chronic Stroke.

medRxiv : the preprint server for health sciences pii:2023.08.26.23294320.

BACKGROUND AND PURPOSE: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation. This study investigated the effectiveness of the IpsiHand System, a contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery affected by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity (proximal and distal), and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.

METHODS: Thirty chronic hemiparetic stroke patients performed a therapeutic BCI task for 12 weeks. Motor function assessment data and resting state electroencephalogram (EEG) signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment (UEFM) served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.

RESULTS: Chronic stroke patients achieved significant motor improvement with BCI therapy. We found significant improvement in both proximal and distal upper extremity motor function. Importantly, motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3 and C4 motor electrodes following BCI therapy. We observed significant positive correlations between motor recovery and theta gamma CFC increase across BCI therapy sessions.

CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients. This therapy was significantly correlated with changes in baseline cortical dynamics, specifically theta-gamma CFC increases in both the right and left motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven motor rehabilitation in chronic stroke patients.

RevDate: 2023-09-10

Huang Y, Deng Y, Kong L, et al (2023)

Vigilant attention mediates the association between resting EEG alpha oscillations and word learning ability.

NeuroImage pii:S1053-8119(23)00520-7 [Epub ahead of print].

Individuals exhibit considerable variability in their capacity to learn and retain new information, including novel vocabulary. Prior research has established the importance of vigilance and electroencephalogram (EEG) alpha rhythm in the learning process. However, the interplay between vigilant attention, EEG alpha oscillations, and an individual's word learning ability (WLA) remains elusive. To address this knowledge gap, here we conducted two experiments with a total of 140 young and middle-aged adults who underwent resting EEG recordings prior to completing a paired-associate word learning task and a psychomotor vigilance test (PVT). The results of both experiments consistently revealed significant positive correlations between WLA and resting EEG alpha oscillations in the occipital and frontal regions. Furthermore, the association between resting EEG alpha oscillations and WLA was mediated by vigilant attention, as measured by the PVT. These findings provide compelling evidence supporting the crucial role of vigilant attention in linking EEG alpha oscillations to an individual's learning ability.

RevDate: 2023-09-09

Tao R, Zhang C, Zhao H, et al (2023)

Active vs. computer-based passive decision-making leads to discrepancies in outcome evaluation: evidence from self-reported emotional experience and brain activity.

Cerebral cortex (New York, N.Y. : 1991) pii:7264118 [Epub ahead of print].

People prefer active decision-making and induce greater emotional feelings than computer-based passive mode, yet the modulation of decision-making mode on outcome evaluation remains unknown. The present study adopted event-related potentials to investigate the discrepancies in active and computer-based passive mode on outcome evaluation using a card gambling task. The subjective rating results showed that active mode elicited more cognitive effort and stronger emotional feelings than passive mode. For received outcomes, we observed no significant Feedback-Related Negativity (FRN) effect on difference waveshapes (d-FRN) between the 2 modes, but active decision-making elicited larger P300 amplitudes than the passive mode. For unchosen card outcomes, the results revealed larger d-FRN amplitudes of relative valences (Superior - Inferior) in responses to negative feedback in active mode than in passive mode. The averaged P300 results revealed an interplay among outcome feedback, decision-making mode, and relative valence, and the average P300 amplitude elicited by the received loss outcome in the active mode partially mediated the relationship between subjective cognitive effort and negative emotion ratings on loss. Our findings indicate discrepancies between active and computer-based passive modes, and cognitive effort and emotional experience involved in outcome evaluation.

RevDate: 2023-09-12

Liyanagedera ND, Hussain AA, Singh A, et al (2023)

Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions.

Brain informatics, 10(1):24.

While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.

RevDate: 2023-09-12
CmpDate: 2023-09-11

Siviero I, Menegaz G, SF Storti (2023)

Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance.

Sensors (Basel, Switzerland), 23(17):.

(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.

RevDate: 2023-09-11

Antony MJ, Sankaralingam BP, Khan S, et al (2023)

Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.

Diagnostics (Basel, Switzerland), 13(17):.

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

RevDate: 2023-09-08

Sun H, Jin J, Daly I, et al (2023)

Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems.

Journal of neuroscience methods pii:S0165-0270(23)00188-7 [Epub ahead of print].

Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.

RevDate: 2023-09-08

Liu K, Yang M, Xing X, et al (2023)

SincMSNet: A Sinc filter convolutional neural network for EEG motor imagery classification.

Journal of neural engineering [Epub ahead of print].

Motor imagery (MI) is a commonly employed experimental paradigm in brain-computer interfaces (BCIs). Nevertheless, the decoding of MI-EEG using convolutional neural networks (CNNs) is still deemed challenging due to the variability of individuals and the non-stationarity of EEG signals. Approach: We propose an end-to-end convolutional neural network (CNN) called SincMSNet for MI decoding. SincMSNet utilizes the Sinc filter to extract subject-specific frequency band information, and mixed-depth convolution to extract multi-scale temporal information for each band. Spatial convolutional blocks are then used to extract spatial features, while the temporal log-variance block is used to acquire classification features. Main results: We assessed SincMSNet on two MI datasets and compared it to several state-of-the-art MI decoding methods. Our results demonstrate that SincMSNet surpasses the benchmark methods, achieving an average accuracy of 80.70% and 71.50% in the four-class and two-class of hold-out classification, respectively. Furthermore, the acquired filter sets exhibit the network's capability to provide higher relevance to individual features. Significance: SincMSNet is a promising method to enhance the performance of MI-EEG decoding, and is available for use through the source code at

RevDate: 2023-09-08

Xiao X, Wang L, Xu M, et al (2023)

A data expansion technique based on training and testing sample to boost the detection of SSVEPs for brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

Currently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy deeply depends on the number of training samples, and the system performance would have a dramatical drop when the training dataset decreased to a small size. To date, no study has been reported to incorporate the unsupervised learning information from testing trails into the construction of supervised classification model, which is a potential way to mitigate the overfitting effect of limited samples. Approach. This study proposed a novel method for SSVEPs detection, i.e., cyclic shift trials (CST), which could combine unsupervised learning information from test trials and supervised learning information from train trials. Furthermore, since SSVEPs are time-locked and phase-locked to the onsets of specific flashes, CST could also expand training samples on the basis of its regularity and periodicity. In order to verify the effectiveness of CST, we designed an online SSVEP-BCI system, and tested this system combined CST with two common classification algorithms, i.e., extended canonical correlation analysis (eCCA) and ensemble task-related component analysis (eTRCA). Main results. CST could significantly enhance the SNRs of SSVEPs and improve the performance of systems especially for the condition of few training samples and short stimulus time. The online information transfer rate (ITR) could reach up to 236.19 bits/min using 36 seconds calibration time of only one training sample for each category. Significance. The proposed CST method can take full advantages of supervised learning information from training samples and unsupervised learning information of testing samples. Furthermore, it is a data expansion technique, which can enhance the SSVEP characteristics and reduce dependence on sample size. Above all, CST is a promising method to improve the performance of SSVEP-based BCI without any additional experimental burden. .

RevDate: 2023-09-08

Semenkov I, Fedosov N, Makarov I, et al (2023)

Real-time low latency estimation of brain rhythms with deep neural networks.

Journal of neural engineering [Epub ahead of print].

Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increase the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits. Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was trained to simultaneously filter and forecast EEG data. We compared it against state-of-the-art techniques using synthetic and real data from 25 subjects. Main results.The Temporal Convolutional Network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios >90% rhythm's envelope correlation with <10 ms effective delay and <20° circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture. Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.

RevDate: 2023-09-11
CmpDate: 2023-09-11

Khan RA, Rashid N, Shahzaib M, et al (2023)

A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

PloS one, 18(9):e0276133.

Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.

RevDate: 2023-09-08

Zhuang S, He M, Feng J, et al (2023)

Near-Infrared Photothermal Manipulates Cellular Excitability and Animal Behavior in Caenorhabditis elegans.

Small methods [Epub ahead of print].

Near-infrared (NIR) photothermal manipulation has emerged as a promising and noninvasive technology for neuroscience research and disease therapy for its deep tissue penetration. NIR stimulated techniques have been used to modulate neural activity. However, due to the lack of suitable in vivo control systems, most studies are limited to the cellular level. Here, a NIR photothermal technique is developed to modulate cellular excitability and animal behaviors in Caenorhabditis elegans in vivo via the thermosensitive transient receptor potential vanilloid 1 (TRPV1) channel with an FDA-approved photothermal agent indocyanine green (ICG). Upon NIR stimuli, exogenous expression of TRPV1 in AFD sensory neurons causes Ca[2+] influx, leading to increased neural excitability and reversal behaviors, in the presence of ICG. The GABAergic D-class motor neurons can also be activated by NIR irradiation, resulting in slower thrashing behaviors. Moreover, the photothermal manipulation is successfully applied in different types of muscle cells (striated muscles and nonstriated muscles), enhancing muscular excitability, causing muscle contractions and behavior changes in vivo. Altogether, this study demonstrates a noninvasive method to precisely regulate the excitability of different types of cells and related behaviors in vivo by NIR photothermal manipulation, which may be applied in mammals and clinical therapy.

RevDate: 2023-09-10

Maslova O, Komarova Y, Shusharina N, et al (2023)

Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review.

Frontiers in human neuroscience, 17:1216648.

The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability to control voluntary muscles, for example, of the upper limbs, which introduces a fundamental dissonance in the possibility of daily use of a computer or smartphone. As a result, the patients lose the ability to communicate with other people. The article presents the most popular paradigms used in the brain-computer-interface speller system and designed for typing by people with severe forms of the movement disorders. Brain-computer interfaces (BCIs) have emerged as a promising technology for individuals with communication impairments. BCI-spellers are systems that enable users to spell words by selecting letters on a computer screen using their brain activity. There are three main types of BCI-spellers: P300, motor imagery (MI), and steady-state visual evoked potential (SSVEP). However, each type has its own limitations, which has led to the development of hybrid BCI-spellers that combine the strengths of multiple types. Hybrid BCI-spellers can improve accuracy and reduce the training period required for users to become proficient. Overall, hybrid BCI-spellers have the potential to improve communication for individuals with impairments by combining the strengths of multiple types of BCI-spellers. In conclusion, BCI-spellers are a promising technology for individuals with communication impairments. P300, MI, and SSVEP are the three main types of BCI-spellers, each with their own advantages and limitations. Further research is needed to improve the accuracy and usability of BCI-spellers and to explore their potential applications in other areas such as gaming and virtual reality.

RevDate: 2023-09-08

Ling Y, Wen X, Tang J, et al (2023)

Effect of topographic comparison of electroencephalographic microstates on the diagnosis and prognosis prediction of patients with prolonged disorders of consciousness.

CNS neuroscience & therapeutics [Epub ahead of print].

AIMS: The electroencephalography (EEG) microstates are indicative of fundamental information processing mechanisms, which are severely damaged in patients with prolonged disorders of consciousness (pDoC). We aimed to improve the topographic analysis of EEG microstates and explore indicators available for diagnosis and prognosis prediction of patients with pDoC, which were still lacking.

METHODS: We conducted EEG recordings on 59 patients with pDoC and 32 healthy controls. We refined the microstate method to accurately estimate topographical differences, and then classify and forecast the prognosis of patients with pDoC. An independent dataset was used to validate the conclusion.

RESULTS: Through optimized topographic analysis, the global explained variance (GEV) of microstate E increased significantly in groups with reduced levels of consciousness. However, its ability to classify the VS/UWS group was poor. In addition, the optimized GEV of microstate E exhibited a statistically significant decrease in the good prognosis group as opposed to the group with a poor prognosis. Furthermore, the optimized GEV of microstate E strongly predicted a patient's prognosis.

CONCLUSION: This technique harmonizes with the existing microstate analysis and exhibits precise and comprehensive differences in microstate topography between groups. Furthermore, this method has significant potential for evaluating the clinical prognosis of pDoC patients.

RevDate: 2023-09-08

Schnitzer SA, DeFilippis DM, Aguilar A, et al (2023)

Maximum stem diameter predicts liana population demography.

Ecology [Epub ahead of print].

Determining population demographic rates is fundamental to understanding differences in species life-history strategies and their capacity to coexist. Calculating demographic rates, however, is challenging and requires long-term, large-scale censuses. Body size may serve as a simple predictor of demographic rate; can it act as a proxy for demographic rate when those data are unavailable? We tested the hypothesis that maximum body size predicts species' demographic rate using repeated censuses of the 77 most common liana species on the Barro Colorado Island, Panama (BCI) 50-ha plot. We found that maximum stem diameter does predict species' population turnover and demography. We also found that lianas on BCI can grow to the enormous diameter of 635 mm, indicating that they can store large amounts of carbon and compete intensely with tropical canopy trees. This study is the first to show that maximum stem diameter can predict plant species' demographic rates and that lianas can attain extremely large diameters. Understanding liana demography is particularly timely because lianas are increasing rapidly in many tropical forests, yet their species-level population dynamics remain chronically understudied. Determining per-species maximum liana diameters in additional forests will enable systematic comparative analyses of liana demography and potential influence across forest types. This article is protected by copyright. All rights reserved.

RevDate: 2023-09-07

Zhu Y, Xie SZ, Peng AB, et al (2023)

Distinct Circuits from Central Lateral Amygdala to Ventral Part of Bed Nucleus of Stria Terminalis Regulate Different Fear Memory.

Biological psychiatry pii:S0006-3223(23)01553-6 [Epub ahead of print].

BACKGROUND: The ability to differentiate stimuli predicting fear is critical for survival, however, the underlying molecular and circuit mechanisms remain poorly understood.

METHODS: We combined transgenic mice, in vivo transsynaptic circuit-dissecting anatomical approaches, optogenetics, pharmacological methods and electrophysiological recording to investigate the involvement of specific extended amygdala circuits in different fear memory.

RESULTS: We identify the projections from central lateral amygdala (CeL) protein kinase C δ (PKCδ) positive neurons and somatostatin (SST) positive neurons to the ventral part of bed nucleus of stria terminalis (vBNST) GABAergic and glutamatergic neurons. Prolonged optogenetic activation or inhibition of PKCδ[CeL-vBNST] pathway specifically reduced context fear memory, whereas SST[CeL-vBNST] pathway mainly reduced tone fear memory. Intriguingly, optogenetic manipulation of vBNST neurons received the projection from PKCδ[CeL] exerted bidirectional regulation of context fear, whereas manipulation of vBNST neurons received the projection from SST[CeL] neurons could bidirectionally regulate both context and tone fear memory. We subsequently demonstrated the presence of δ and κ opioid receptor protein expression within the CeL-vBNST circuits, potentially accounting for the discrepancy between prolonged activation of GABAergic circuits and inhibition of downstream vBNST neurons. Finally, administration of an opioid receptor antagonist cocktail on the PKCδ[CeL-vBNST] or SST[CeL-vBNST] pathway successfully restored context or tone fear memory reduction induced by prolonged activation of the circuits.

CONCLUSIONS: Together, these findings establish a functional role for distinct CeL-vBNST circuits in the differential regulation and appropriate maintenance of fear.

RevDate: 2023-09-07

Barmpas K, Panagakis Y, Adamos DA, et al (2023)

BrainWave-Scattering Net: A lightweight network for EEG-based motor imagery recognition.

Journal of neural engineering [Epub ahead of print].

Brain-computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one's neural activity, measured by electroencephalography (EEG) signals. In recent years, Convolutional Neural Networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available. In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations. We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier. In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.

RevDate: 2023-09-07

Zhang L, Li C, Zhang R, et al (2023)

Online semi-supervised learning for motor imagery EEG classification.

Computers in biology and medicine, 165:107405 pii:S0010-4825(23)00870-3 [Epub ahead of print].

OBJECTIVE: Time-consuming data labeling in brain-computer interfaces (BCIs) raises many problems such as mental fatigue and is one key factor that hinders the real-world adoption of motor imagery (MI)-based BCIs. An alternative approach is to integrate readily available, as well as informative, unlabeled data online, whereas this approach is less investigated.

APPROACH: We proposed an online semi-supervised learning scheme to improve the classification performance of MI-based BCI. This scheme uses regularized weighted online sequential extreme learning machine (RWOS-ELM) as the base classifier and updates its model parameters with incoming balanced data chunk-by-chunk. In the initial stage, we designed a technique that combines the synthetic minority oversampling with the edited nearest neighbor rule for data augmentation to construct more discriminative initial classifiers. When used online, the incoming chunk of data is first pseudo-labeled by RWOS-ELM as well as an auxiliary classifier, and then balanced again by the above-mentioned technique. Initial classifiers are further updated based on these class-balanced data.

MAIN RESULTS: Offline experimental results on two publicly available MI datasets demonstrate the superiority of the proposed scheme over its counterparts. Further online experiments on six subjects show that their BCI performance gradually improved by learning from incoming unlabeled data.

SIGNIFICANCE: Our proposed online semi-supervised learning scheme has higher computation and memory usage efficiency, which is promising for online MI-based BCIs, especially in the case of insufficient labeled training data.

RevDate: 2023-09-07

Jiang Y, Yin J, Zhao B, et al (2023)

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke.

Journal of visualized experiments : JoVE.

The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research owing to the difficulties encountered. Brain-computer interface (BCI) represents a hot frontier technology in brain neuroscience research. It refers to the direct conversion of the sensory perception, imagery, cognition, and thinking of users or subjects into actions, without reliance on peripheral nerves or muscles, to establish direct communication and control channels between the brain and external devices. Motor imagery brain-computer interface (MI-BCI) is the most common clinical application of rehabilitation as a non-invasive means of rehabilitation. Previous clinical studies have confirmed that MI-BCI positively improves motor dysfunction in patients after stroke. However, there is a lack of clinical operation demonstration. To that end, this study describes in detail the treatment of MI-BCI for patients with moderate and severe upper limb dysfunction after stroke and shows the intervention effect of MI-BCI through clinical function evaluation and brain function evaluation results, thereby providing ideas and references for clinical rehabilitation application and mechanism research.

RevDate: 2023-09-07

Luo Y, Sun C, Wei M, et al (2023)

Integrated Flexible Microscale Mechanical Sensors Based on Cascaded Free Spectral Range-Free Cavities.

Nano letters [Epub ahead of print].

Photonic mechanical sensors offer several advantages over their electronic counterparts, including immunity to electromagnetic interference, increased sensitivity, and measurement accuracy. Exploring flexible mechanical sensors on deformable substrates provides new opportunities for strain-optical coupling operations. Nevertheless, existing flexible photonics strategies often require cumbersome signal collection and analysis with bulky setups, limiting their portability and affordability. To address these challenges, we propose a waveguide-integrated flexible mechanical sensor based on cascaded photonic crystal microcavities with inherent deformation and biaxial tensile state analysis. Leveraging the advanced multiplexing capability of the sensor, for the first time, we successfully demonstrate 2D shape reconstruction and quasi-distributed strain sensing with 110 μm spatial resolution. Our microscale mechanical sensor also exhibits exceptional sensitivity with a detected force level as low as 13.6 μN in real-time measurements. This sensing platform has potential applications in various fields, including biomedical sensing, surgical catheters, aircraft and spacecraft engineering, and robotic photonic skin development.

RevDate: 2023-09-08

Lugo ZR, Cinel C, Jeunet C, et al (2023)

Editorial: Women in brain-computer interfaces.

Frontiers in human neuroscience, 17:1260479.

RevDate: 2023-09-07
CmpDate: 2023-09-07

Zrenner B, Zrenner C, Balderston N, et al (2023)

Toward personalized circuit-based closed-loop brain-interventions in psychiatry: using symptom provocation to extract EEG-markers of brain circuit activity.

Frontiers in neural circuits, 17:1208930.

Symptom provocation is a well-established component of psychiatric research and therapy. It is hypothesized that specific activation of those brain circuits involved in the symptomatic expression of a brain pathology makes the relevant neural substrate accessible as a target for therapeutic interventions. For example, in the treatment of obsessive-compulsive disorder (OCD), symptom provocation is an important part of psychotherapy and is also performed prior to therapeutic brain stimulation with transcranial magnetic stimulation (TMS). Here, we discuss the potential of symptom provocation to isolate neurophysiological biomarkers reflecting the fluctuating activity of relevant brain networks with the goal of subsequently using these markers as targets to guide therapy. We put forward a general experimental framework based on the rapid switching between psychiatric symptom states. This enable neurophysiological measures to be derived from EEG and/or TMS-evoked EEG measures of brain activity during both states. By subtracting the data recorded during the baseline state from that recorded during the provoked state, the resulting contrast would ideally isolate the specific neural circuits differentially activated during the expression of symptoms. A similar approach enables the design of effective classifiers of brain activity from EEG data in Brain-Computer Interfaces (BCI). To obtain reliable contrast data, psychiatric state switching needs to be achieved multiple times during a continuous recording so that slow changes of brain activity affect both conditions equally. This is achieved easily for conditions that can be controlled intentionally, such as motor imagery, attention, or memory retention. With regard to psychiatric symptoms, an increase can often be provoked effectively relatively easily, however, it can be difficult to reliably and rapidly return to a baseline state. Here, we review different approaches to return from a provoked state to a baseline state and how these may be applied to different symptoms occurring in different psychiatric disorders.

RevDate: 2023-09-06

Kumawat J, Yadav A, Yadav K, et al (2023)

Comparison of Spectral Analysis of Gamma Band Activity During Actual and Imagined Movements as a Cognitive Tool.

Clinical EEG and neuroscience [Epub ahead of print].

Background. Imagined motor movement is a cognitive process in which a subject imagines a movement without doing it, which activates similar brain regions as during actual motor movement. Brain gamma band activity (GBA) is linked to cognitive functions such as perception, attention, memory, awareness, synaptic plasticity, motor control, and Imagination. Motor imagery can be used in sports to improve performance, raising the possibility of using it as a rehabilitation method through brain plasticity through mirror neurons. Method. A comparative observational study was conducted on 56 healthy male subjects after obtaining clearance from the Ethics Committee. EEG recordings for GBA were taken for resting, real, and imaginary motor movements and compared. The power spectrum of gamma waves was analyzed using the Kruskal-Wallis test; a p-value <.05 was considered significant. Results. The brain gamma rhythm amplitude was statistically increased during both actual and imaginary motor movement compared to baseline (resting stage) in most of the regions of the brain except the occipital region. There was no significant difference in GBA between real and imaginary movements. Conclusions. Increased gamma rhythm amplitude during both actual and imaginary motor movement than baseline (resting stage) indicating raised brain cognitive activity during both types of movements. There was no potential difference between real and imaginary movements suggesting that the real movement can be replaced by the imaginary movement to enhance work performance through mirror therapy.

RevDate: 2023-09-06

Lima EO, Silva LM, Melo ALV, et al (2023)

Transcranial Direct Current Stimulation and Brain-Computer Interfaces for Improving Post-Stroke Recovery: A Systematic Review and Meta-Analysis.

Clinical rehabilitation [Epub ahead of print].

OBJECTIVE: This study aimed to evaluate the effectiveness of transcranial direct current stimulation associated with brain-computer interface in stroke patients.

DATA SOURCES: The PubMed, Central, PEDro, Web of Science, SCOPUS, PsycINFO Ovid, CINAHL EBSCO, EMBASE, and ScienceDirect databases were searched from inception to April 2023 for randomized controlled studies reporting the effects of active transcranial direct current stimulation associated with brain-computer interface to a transcranial direct current stimulation sham associated with brain-computer interface condition on the outcome measure (motor performance and functional independence).

REVIEW METHODS: We searched for full-text articles which had investigated the effect of transcranial direct current stimulation associated with brain-computer interface on motor performance in the upper extremities in stroke patients. The standardized mean differences derived from the change in scores between pretreatment and post-treatment were adopted as the effect size measure, with a 95% confidence interval. Possible sources of heterogeneity were analyzed by performing subgroup analyses in order to examine the moderating effects for one variable: the level of injury severity.

RESULTS: Nine studies were included in the qualitative synthesis and the meta-analysis. The findings of the conducted analyses indicated there is not enough evidence to suggest that active transcranial direct current stimulation associated with brain-computer interface is more efficient in motor performance and functional independence when compared to sham transcranial direct current stimulation associated with brain-computer interface or brain-computer interface alone. In addition, the quality of evidence was rated very low. A subgroup analysis was performed for the motor performance outcome considering the injury severity level.

CONCLUSION: We found evidence that transcranial direct current stimulation associated with brain-computer interface was not more beneficial than sham transcranial direct current stimulation associated with brain-computer interface or brain-computer interface alone.

RevDate: 2023-09-08
CmpDate: 2023-09-07

Dreyer P, Roc A, Pillette L, et al (2023)

A large EEG database with users' profile information for motor imagery brain-computer interface research.

Scientific data, 10(1):580.

We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which represents more than 20,800 trials, or approximately 70 hours of recording time. It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: (1) studying the relationships between BCI users' profiles and their BCI performances, (2) studying how EEG signals properties varies for different users' profiles and MI tasks, (3) using the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporating users' profile information into the design of EEG signal classification algorithms.

RevDate: 2023-09-07

Ramirez-Nava AG, Mercado-Gutierrez JA, Quinzaños-Fresnedo J, et al (2023)

Functional electrical stimulation therapy controlled by a P300-based brain-computer interface, as a therapeutic alternative for upper limb motor function recovery in chronic post-stroke patients. A non-randomized pilot study.

Frontiers in neurology, 14:1221160.

INTRODUCTION: Up to 80% of post-stroke patients present upper-limb motor impairment (ULMI), causing functional limitations in daily activities and loss of independence. UMLI is seldom fully recovered after stroke when using conventional therapeutic approaches. Functional Electrical Stimulation Therapy (FEST) controlled by Brain-Computer Interface (BCI) is an alternative that may induce neuroplastic changes, even in chronic post-stroke patients. The purpose of this work was to evaluate the effects of a P300-based BCI-controlled FEST intervention, for ULMI recovery of chronic post-stroke patients.

METHODS: A non-randomized pilot study was conducted, including 14 patients divided into 2 groups: BCI-FEST, and Conventional Therapy. Assessments of Upper limb functionality with Action Research Arm Test (ARAT), performance impairment with Fugl-Meyer assessment (FMA), Functional Independence Measure (FIM) and spasticity through Modified Ashworth Scale (MAS) were performed at baseline and after carrying out 20 therapy sessions, and the obtained scores compared using Chi square and Mann-Whitney U statistical tests (𝛼 = 0.05).

RESULTS: After training, we found statistically significant differences between groups for FMA (p = 0.012), ARAT (p < 0.001), and FIM (p = 0.025) scales.

DISCUSSION: It has been shown that FEST controlled by a P300-based BCI, may be more effective than conventional therapy to improve ULMI after stroke, regardless of chronicity.

CONCLUSION: The results of the proposed BCI-FEST intervention are promising, even for the most chronic post-stroke patients often relegated from novel interventions, whose expected recovery with conventional therapy is very low. It is necessary to carry out a randomized controlled trial in the future with a larger sample of patients.

RevDate: 2023-09-05

Gomez-Andres A, Cerda-Company X, Cucurell D, et al (2023)

Decoding agency attribution using single trial error-related brain potentials.

Psychophysiology [Epub ahead of print].

Being able to distinguish between self and externally generated actions is a key factor influencing learning and adaptive behavior. Previous literature has highlighted that whenever a person makes or perceives an error, a series of error-related potentials (ErrPs) can be detected in the electroencephalographic (EEG) signal, such as the error-related negativity (ERN) component. Recently, ErrPs have gained a lot of interest for the use in brain-computer interface (BCI) applications, which give the user the ability to communicate by means of decoding his/her brain activity. Here, we explored the feasibility of employing a support vector machine classifier to accurately disentangle self-agency errors from other-agency errors from the EEG signal at a single-trial level in a sample of 23 participants. Our results confirmed the viability of correctly disentangling self/internal versus other/external agency-error attributions at different stages of brain processing based on the latency and the spatial topographical distribution of key ErrP features, namely, the ERN and P600 components, respectively. These results offer a new perspective on how to distinguish self versus externally generated errors providing new potential implementations on BCI systems.

RevDate: 2023-09-05

Kheirabadi R, H Omranpour (2023)

Learning classifiers in clustered data: BCI pattern recognition model for EEG-based human emotion recognition.

Computer methods in biomechanics and biomedical engineering [Epub ahead of print].

Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition (EMD) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions (IMF), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy.

RevDate: 2023-09-06
CmpDate: 2023-09-06

Li Q, Zhang T, Song Y, et al (2023)

[A design and evaluation of wearable p300 brain-computer interface system based on Hololens2].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 40(4):709-717.

Patients with amyotrophic lateral sclerosis (ALS) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.

RevDate: 2023-09-06
CmpDate: 2023-09-06

Li K, Lu J, Yu R, et al (2023)

[Alterations of β-γ coupling of scalp electroencephalography during epilepsy].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 40(4):700-708.

Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.

RevDate: 2023-09-06
CmpDate: 2023-09-06

Luo R, Dou X, Xiao X, et al (2023)

[Recognition of high-frequency steady-state visual evoked potential for brain-computer interface].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 40(4):683-691.

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.

RevDate: 2023-09-04

Lim J, Wang PT, Bashford L, et al (2023)

Suppression of cortical electrostimulation artifacts using pre-whitening and null projection.

Journal of neural engineering [Epub ahead of print].

Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods. Approach: We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects. Main results: In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78-80\% and 85\%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement. Significance: PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional brain-computer interfaces to biomimetically restore motor function. .

RevDate: 2023-09-04

Sun Y, Shen A, Du C, et al (2023)

A Real-Time Non-Implantation Bi-Directional Brain-Computer Interface Solution without Stimulation Artifacts.

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 non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by 4.59 kΩ, about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future.

RevDate: 2023-09-05

Cui J, Yuan L, Wang Z, et al (2023)

Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.

Frontiers in computational neuroscience, 17:1232925.

INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions.

METHODS: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI.

RESULTS: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results.

DISCUSSION: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.

RevDate: 2023-09-05

Huang Y, Zheng J, Xu B, et al (2023)

An improved model using convolutional sliding window-attention network for motor imagery EEG classification.

Frontiers in neuroscience, 17:1204385.

INTRODUCTION: The classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability.

METHODS: To solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks.

RESULTS: The model outperformed existing state-of-the-art (SOTA) models in within- and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22 and 2.02%, respectively.

DISCUSSION: The experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head self-attention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation.

RevDate: 2023-09-05

Zhang J, Li K, Yang B, et al (2023)

Local and global convolutional transformer-based motor imagery EEG classification.

Frontiers in neuroscience, 17:1219988.

Transformer, a deep learning model with the self-attention mechanism, combined with the convolution neural network (CNN) has been successfully applied for decoding electroencephalogram (EEG) signals in Motor Imagery (MI) Brain-Computer Interface (BCI). However, the extremely non-linear, nonstationary characteristics of the EEG signals limits the effectiveness and efficiency of the deep learning methods. In addition, the variety of subjects and the experimental sessions impact the model adaptability. In this study, we propose a local and global convolutional transformer-based approach for MI-EEG classification. The local transformer encoder is combined to dynamically extract temporal features and make up for the shortcomings of the CNN model. The spatial features from all channels and the difference in hemispheres are obtained to improve the robustness of the model. To acquire adequate temporal-spatial feature representations, we combine the global transformer encoder and Densely Connected Network to improve the information flow and reuse. To validate the performance of the proposed model, three scenarios including within-session, cross-session and two-session are designed. In the experiments, the proposed method achieves up to 1.46%, 7.49% and 7.46% accuracy improvement respectively in the three scenarios for the public Korean dataset compared with current state-of-the-art models. For the BCI competition IV 2a dataset, the proposed model also achieves a 2.12% and 2.21% improvement for the cross-session and two-session scenarios respectively. The results confirm that the proposed approach can effectively extract much richer set of MI features from the EEG signals and improve the performance in the BCI applications.

RevDate: 2023-09-02

Yu H, Qi Y, G Pan (2023)

NeuSort: an automatic adaptive spike sorting approach with neuromorphic models.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons.

APPROACH: NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters.

RESULTS: Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation.

SIGNIFICANCE: NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.

RevDate: 2023-09-02

Zhang D, Li H, Xie J, et al (2023)

MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals.

Neural networks : the official journal of the International Neural Network Society, 167:183-198 pii:S0893-6080(23)00427-6 [Epub ahead of print].

Non-stationarity of EEG signals leads to high variability between subjects, making it challenging to directly use data from other subjects (source domain) for the classifier in the current subject (target domain). In this study, we propose MI-DAGSC to address domain adaptation challenges in EEG-based motor imagery (MI) decoding. By combining domain-level information, class-level information, and inter-sample structure information, our model effectively aligns the feature distributions of source and target domains. This work is an extension of our previous domain adaptation work MI-DABAN (Li et al., 2023). Based on MI-DABAN, MI-DAGSC designs Sample-Feature Blocks (SFBs) and Graph Convolution Blocks (GCBs) to focus on intra-sample and inter-sample information. The synergistic integration of SFBs and GCBs enable the model to capture comprehensive information and understand the relationship between samples, thus improving representation learning. Furthermore, we introduce a triplet loss to enhance the alignment and compactness of feature representations. Extensive experiments on real EEG datasets demonstrate the effectiveness of MI-DAGSC, confirming that our method makes a valuable contribution to the MI-EEG decoding. Moreover, it holds great potential for various applications in brain-computer interface systems and neuroscience research. And the code of the proposed architecture in this study is available under

RevDate: 2023-09-01

Leinders S, Vansteensel MJ, Piantoni G, et al (2023)

Using fMRI to localize target regions for implanted brain-computer interfaces in locked-in syndrome.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 155:1-15 pii:S1388-2457(23)00700-9 [Epub ahead of print].

OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have the potential to improve quality of life of people with locked-in syndrome (LIS) by restoring their ability to communicate independently. Before implantation of such a system, it is important to localize ECoG electrode target regions. Here, we assessed the predictive value of functional magnetic resonance imaging (fMRI) for the localization of suitable target regions on the sensorimotor cortex for ECoG-based BCI in people with locked-in syndrome.

METHODS: Three people with locked-in syndrome were implanted with a chronic, fully implantable ECoG-BCI system. We compared pre-surgical fMRI activity with post-implantation ECoG activity from areas known to be active and inactive during attempted hand movement (sensorimotor hand region and dorsolateral prefrontal cortex, respectively).

RESULTS: Results showed a spatial match between fMRI activity and changes in ECoG low and high frequency band power (10 - 30 and 65 - 95 Hz, respectively) during attempted movement. Also, we found that fMRI can be used to select a sub-set of electrodes that show strong task-related signal changes that are therefore likely to generate adequate BCI control.

CONCLUSIONS: Our findings indicate that fMRI is a useful non-invasive tool for the pre-surgical workup of BCI implant candidates.

SIGNIFICANCE: If these results are confirmed in more BCI studies, fMRI might be used for more efficient surgical BCI procedures with focused cortical coverage and lower participant burden.

RevDate: 2023-08-31

Gu B, Wang K, Chen L, et al (2023)

Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities.

Neuroscience pii:S0306-4522(23)00401-3 [Epub ahead of print].

Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.

RevDate: 2023-08-31

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

User Identity Protection in EEG-based Brain-Computer Interfaces: Supplementary Material.

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

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.

RevDate: 2023-09-01

Vargas G, Araya D, Sepulveda P, et al (2023)

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task.

Frontiers in neuroscience, 17:1212549.

INTRODUCTION: Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.

METHODS: We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.

RESULTS: Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.

DISCUSSION: The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

RevDate: 2023-09-03

Sun Y, Zabihi M, Li Q, et al (2023)

Drug Permeability: From the Blood-Brain Barrier to the Peripheral Nerve Barriers.

Advanced therapeutics, 6(4):.

Drug delivery into the peripheral nerves and nerve roots has important implications for effective local anesthesia and treatment of peripheral neuropathies and chronic neuropathic pain. Similar to drugs that need to cross the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB) to gain access to the central nervous system (CNS), drugs must cross the peripheral nerve barriers (PNB), formed by the perineurium and blood-nerve barrier (BNB) to modulate peripheral axons. Despite significant progress made to develop effective strategies to enhance BBB permeability in therapeutic drug design, efforts to enhance drug permeability and retention in peripheral nerves and nerve roots are relatively understudied. Guided by knowledge describing structural, molecular and functional similarities between restrictive neural barriers in the CNS and peripheral nervous system (PNS), we hypothesize that certain CNS drug delivery strategies are adaptable for peripheral nerve drug delivery. In this review, we describe the molecular, structural and functional similarities and differences between the BBB and PNB, summarize and compare existing CNS and peripheral nerve drug delivery strategies, and discuss the potential application of selected CNS delivery strategies to improve efficacious drug entry for peripheral nerve disorders.

RevDate: 2023-09-01

Wang H, Qi Y, Yao L, et al (2023)

A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.

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

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals toward an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we first model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution and 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over coadaptive approaches in both learning efficiency and effectiveness.

RevDate: 2023-09-04

Natraj N, Seko S, Abiri R, et al (2023)

Flexible regulation of representations on a drifting manifold enables long-term stable complex neuroprosthetic control.

bioRxiv : the preprint server for biology.

The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what is the representational stability of simple actions, particularly those that are well-rehearsed in humans, and how it changes in new contexts. Using an electrocorticography brain-computer interface (BCI), we found that the mesoscale manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. Interestingly, however, the manifold's absolute location demonstrated day-to-day drift. Strikingly, representational statistics, especially variance, could be flexibly regulated to increase discernability during BCI control without somatotopic changes. Discernability strengthened with practice and was specific to the BCI, demonstrating remarkable contextual specificity. Accounting for drift, and leveraging the flexibility of representations, allowed neuroprosthetic control of a robotic arm and hand for over 7 months without recalibration. Our study offers insight into how electrocorticography can both track representational statistics across long periods and allow long-term complex neuroprosthetic control.

RevDate: 2023-09-02

Martinez DRQ, Rubio GF, Bonetti L, et al (2023)

Decoding reveals the neural representation of held and manipulated musical thoughts.

bioRxiv : the preprint server for biology.

UNLABELLED: Imagine a song you know by heart. With little effort you could sing it or play it vividly in your mind. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts". Here, we decoded listened and imagined melodies from MEG brain data (N = 71) to show that auditory regions represent the sensory properties of individual sounds, whereas cognitive control (prefrontal cortex, basal nuclei, thalamus) and episodic memory areas (inferior and medial temporal lobe, posterior cingulate, precuneus) hold and manipulate the melody as an abstract unit. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting the volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations and paves the way for future work on neural decoding of auditory imagination.

SIGNIFICANCE STATEMENT: Imagining vividly a sequence of sounds is a skill that most humans exert with relatively little effort. However, it is unknown how the brain achieves such an outstanding feat. Here, we used decoding techniques and non-invasive electrophysiology to investigate how sequences of sounds are represented in the brain. We report that auditory regions represent the sensory properties of individual sounds while association areas represent melodies as abstract entities. Moreover, we show that mentally manipulating a melody changes its neural representation across the brain. Understanding auditory representations and their volitional control opens the path for future work on decoding of imagined auditory objects and possible applications in cognitive brain computer interfaces.

RevDate: 2023-08-31

Park HJ, B Lee (2023)

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network.

Frontiers in human neuroscience, 17:1186594.

INTRODUCTION: In this study, we classified electroencephalography (EEG) data of imagined speech using signal decomposition and multireceptive convolutional neural network. The imagined speech EEG with five vowels /a/, /e/, /i/, /o/, and /u/, and mute (rest) sounds were obtained from ten study participants.

MATERIALS AND METHODS: First, two different signal decomposition methods were applied for comparison: noise-assisted multivariate empirical mode decomposition and wavelet packet decomposition. Six statistical features were calculated from the decomposed eight sub-frequency bands EEG. Next, all features obtained from each channel of the trial were vectorized and used as the input vector of classifiers. Lastly, EEG was classified using multireceptive field convolutional neural network and several other classifiers for comparison.

RESULTS: We achieved an average classification rate of 73.09 and up to 80.41% in a multiclass (six classes) setup (Chance: 16.67%). In comparison with various other classifiers, significant improvements for other classifiers were achieved (p-value < 0.05). From the frequency sub-band analysis, high-frequency band regions and the lowest-frequency band region contain more information about imagined vowel EEG data. The misclassification and classification rate of each vowel imaginary EEG was analyzed through a confusion matrix.

DISCUSSION: Imagined speech EEG can be classified successfully using the proposed signal decomposition method and a convolutional neural network. The proposed classification method for imagined speech EEG can contribute to developing a practical imagined speech-based brain-computer interfaces system.

RevDate: 2023-08-29

Dong Y, Tang X, Li Q, et al (2023)

An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network.

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

Electroencephalogram (EEG) recordings often contain artifacts that would lower signal quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most of them rely on visual inspection and manual operations, which is time/labor-consuming, subjective, and incompatible to filter massive EEG data in real-time. In this paper, we proposed a deep learning framework named Artifact Removal Wasserstein Generative Adversarial Network (AR-WGAN), where the well-trained model can decompose input EEG, detect and delete artifacts, and then reconstruct denoised signals within a short time. The proposed approach was systematically compared with commonly used denoising methods including Denoised AutoEncoder, Wiener Filter, and Empirical Mode Decomposition, with both public and self-collected datasets. The experimental results proved the promising performance of AR-WGAN on automatic artifact removal for massive data across subjects, with correlation coefficient up to 0.726±0.033, and temporal and spatial relative root-mean-square error as low as 0.176±0.046 and 0.761±0.046, respectively. This work may demonstrate the proposed AR-WGAN as a high-performance end-to-end method for EEG denoising, with many on-line applications in clinical EEG monitoring and brain-computer interfaces.


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 )