Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis

EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training

A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.

Characterization and classification of kinesthetic motor imagery levels

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Brain (EEG) and muscle (EMG) activity related to 3D sit-to-stand kinematics in healthy adults and in central neurological pathology - A systematic review

BACKGROUND

The sit-to-stand transfer is a fundamental functional movement during normal activities of daily living. Central nervous system disorders can negatively impact the execution of sit-to-stand transfers, often impeding successful completion. Despite its importance, the neurophysiological basis at muscle (electromyography (EMG)) and brain (electroencephalography (EEG)) level as related to the kinematic movement is not well understood.

OBJECTIVES

This review synthesises the published literature addressing central and peripheral neural activity during 3D kinematic capture of sit-to-stand transfers.

METHODS

A pre-registered systematic review was conducted. Electronic databases (PubMed, CINAHL Plus, Web of Science, Scopus and EMBASE) were searched from inception using search operators that included sit-to-stand, kinematics and EMG and/or EEG. The search was not limited by study type but was limited to populations comprising of healthy individuals or individuals with a central neurological pathology.

RESULTS

From a total of 28,770 identified papers, 59 were eligible for inclusion. Ten of these 59 studies received a moderate quality rating; with the remainder rated as weak using the Effective Public Health Practice Project tool. Fifty-eight studies captured kinematic data of sit-to-stand with associated EMG activity only and one study captured kinematics with co-registered EMG and EEG data. Fifty-six studies examined sit-to-stand transfer in healthy individuals, reporting four dynamic movement phases and three muscle synergies commonly used by most individuals to stand-up. Pre-movement EEG activity was reported in one study with an absence of data during execution. Eight studies examined participants following stroke and two examined participants with Parkinson's disease, both reporting no statistically significant differences between their kinematics and muscle activity and those of healthy controls.

SIGNIFICANCE

Little is known about the neural basis of the sit-to-stand transfer at brain level with limited focus in central neurological pathology. This poses a barrier to targeted mechanistic-based rehabilitation of the sit-to-stand movement in neurological populations.

Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system

This study explores the efficacy of our novel and personalized brain-computer interface (BCI) therapy, in enhancing hand movement recovery among stroke survivors. Stroke often results in impaired motor function, posing significant challenges in daily activities and leading to considerable societal and economic burdens. Traditional physical and occupational therapies have shown limitations in facilitating satisfactory recovery for many patients. In response, our study investigates the potential of motor imagery-based BCIs (MI-BCIs) as an alternative intervention. In this study, MI-BCIs translate imagined hand movements into actions using a combination of scalp-recorded electrical brain activity and signal processing algorithms. Our prior research on MI-BCIs, which emphasizes the benefits of proprioceptive feedback over traditional visual feedback and the importance of customizing the delay between brain activation and passive hand movement, led to the development of RehabSwift therapy. In this study, we recruited 12 chronic-stage stroke survivors to assess the effectiveness of our solution. The primary outcome measure was the Fugl-Meyer upper extremity (FMA-UE) assessment, complemented by secondary measures including the action research arm test, reaction time, unilateral neglect, spasticity, grip and pinch strength, goal attainment scale, and FMA-UE sensation. Our findings indicate a remarkable improvement in hand movement and a clinically significant reduction in poststroke arm and hand impairment following 18 sessions of neurofeedback training. The effects persisted for at least 4 weeks posttreatment. These results underscore the potential of MI-BCIs, particularly our solution, as a prospective tool in stroke rehabilitation, offering a personalized and adaptable approach to neurofeedback training.

Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial

BACKGROUND

Upper limb motor dysfunction is a major problem in the rehabilitation of patients with stroke. Brain-computer interface (BCI) is a kind of communication system that converts the "ideas" in the brain into instructions and has been used in stroke rehabilitation. This study aimed to investigate the efficacy and safety of BCI in rehabilitation training on upper limb motor function among patients with ischemic stroke.

METHODS

This was an investigator-initiated, multicenter, randomized, open-label, blank-controlled clinical trial with blinded outcome assessment conducted at 17 centers in China. Patients were assigned in a 1:1 ratio to the BCI group or the control group based on traditional rehabilitation training. The primary efficacy outcome is the difference in improvement of the Fugl-Meyer Assessment upper extremity (FMA-UE) score between two groups at month 1 after randomization. The safety outcomes were any adverse events within 3 months.

FINDINGS

A total of 296 patients with ischemic stroke were enrolled and randomly allocated to the BCI group (n = 150) and the control group (n = 146). The primary efficacy outcomes of FMA-UE score change from baseline to 1 month were 13.17 (95% confidence interval [CI], 11.56-14.79) in the BCI group and 9.83 (95% CI, 8.19-11.47) in the control group (mean difference between groups was 3.35; 95% CI, 1.05-5.65; p = 0.0045). Adverse events occurred in 33 patients (22.00%) in the BCI group and in 31 patients (21.23%) in the control group.

CONCLUSIONS

BCI rehabilitation training can further improve upper limb motor function based on traditional rehabilitation training in patients with ischemic stroke. This study was registered at ClinicalTrials.gov: NCT04387474.

FUNDING

This work was supported by the National Key R&D Program of China (2018YFC1312903), the National Key Research and Development Program of China (2022YFC3600600), the Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (CCMU2022ZKYXZ009), the Beijing Natural Science Foundation Haidian original innovation joint fund (L222123), the Fund for Young Talents of Beijing Medical Management Center (QML20230505), and the high-level public health talents (xuekegugan-02-47).

Application of emerging technologies in ischemic stroke: from clinical study to basic research

Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke.

Peripheral nerve transfers for dysfunctions in central nervous system injuries: a systematic review

BACKGROUND

The review highlights recent advancements and innovative uses of nerve transfer surgery in treating dysfunctions caused by central nervous system (CNS) injuries, with a particular focus on spinal cord injury (SCI), stroke, traumatic brain injury, and cerebral palsy.

METHODS

A comprehensive literature search was conducted regarding nerve transfer for restoring sensorimotor functions and bladder control following injuries of spinal cord and brain, across PubMed and Web of Science from January 1920 to May 2023. Two independent reviewers undertook article selection, data extraction, and risk of bias assessment with several appraisal tools, including the Cochrane Risk of Bias Tool, the JBI Critical Appraisal Checklist, and SYRCLE's ROB tool. The study protocol has been registered and reported following PRISMA and AMSTAR guidelines.

RESULTS

Nine hundred six articles were retrieved, of which 35 studies were included (20 on SCI and 15 on brain injury), with 371 participants included in the surgery group and 192 in the control group. These articles were mostly low-risk, with methodological concerns in study types, highlighting the complexity and diversity. For SCI, the strength of target muscle increased by 3.13 of Medical Research Council grade, and the residual urine volume reduced by more than 100 ml in 15 of 20 patients. For unilateral brain injury, the Fugl-Myer motor assessment (FMA) improved 15.14-26 score in upper extremity compared to 2.35-26 in the control group. The overall reduction in Modified Ashworth score was 0.76-2 compared to 0-1 in the control group. Range of motion (ROM) increased 18.4-80° in elbow, 20.4-110° in wrist and 18.8-130° in forearm, while ROM changed -4.03°-20° in elbow, -2.08°-10° in wrist, -2.26°-20° in forearm in the control group. The improvement of FMA in lower extremity was 9 score compared to the presurgery.

CONCLUSION

Nerve transfer generally improves sensorimotor functions in paralyzed limbs and bladder control following CNS injury. The technique effectively creates a 'bypass' for signals and facilitates functional recovery by leveraging neural plasticity. It suggested a future of surgery, neurorehabilitation and robotic-assistants converge to improve outcomes for CNS.

Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays

AIM OF THE STUDY

Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms.

MATERIALS AND METHODS

16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and µECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel µECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such.

RESULTS

The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': -0.11) had higher bias for the right lever than Rat 1 (B'': - 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine.

CONCLUSION

According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.

TASA: Temporal Attention With Spatial Autoencoder Network for Odor-Induced Emotion Classification Using EEG

The olfactory system enables humans to smell different odors, which are closely related to emotions. The high temporal resolution and non-invasiveness of Electroencephalogram (EEG) make it suitable to objectively study human preferences for odors. Effectively learning the temporal dynamics and spatial information from EEG is crucial for detecting odor-induced emotional valence. In this paper, we propose a deep learning architecture called Temporal Attention with Spatial Autoencoder Network (TASA) for predicting odor-induced emotions using EEG. TASA consists of a filter-bank layer, a spatial encoder, a time segmentation layer, a Long Short-Term Memory (LSTM) module, a multi-head self-attention (MSA) layer, and a fully connected layer. We improve upon the previous work by utilizing a two-phase learning framework, using the autoencoder module to learn the spatial information among electrodes by reconstructing the given input with a latent representation in the spatial dimension, which aims to minimize information loss compared to spatial filtering with CNN. The second improvement is inspired by the continuous nature of the olfactory process; we propose to use LSTM-MSA in TASA to capture its temporal dynamics by learning the intercorrelation among the time segments of the EEG. TASA is evaluated on an existing olfactory EEG dataset and compared with several existing deep learning architectures to demonstrate its effectiveness in predicting olfactory-triggered emotional responses. Interpretability analyses with DeepLIFT also suggest that TASA learns spatial-spectral features that are relevant to olfactory-induced emotion recognition.

Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions

Introduction

This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments.

Methods

It examined 3,626 journal articles from the Web of Science (WoS) using co-citation and co-word methods.

Results

The co-citation analysis identified three major clusters: "Real-Time fMRI Neurofeedback and Self-Regulation of Brain Activity," "EEG Neurofeedback and Cognitive Performance Enhancement," and "Treatment of ADHD Using Neurofeedback." The co-word analysis highlighted four key clusters: "Neurofeedback in Mental Health Research," "Brain-Computer Interfaces for Stroke Rehabilitation," "Neurofeedback for ADHD in Youth," and "Neural Mechanisms of Emotion and Self-Regulation with Advanced Neuroimaging.

Discussion

This in-depth bibliometric study significantly enhances our understanding of the dynamic field of neurofeedback, indicating its potential in treating ADHD and improving performance. It offers non-invasive, ethical alternatives to conventional psychopharmacology and aligns with the trend toward personalized medicine, suggesting specialized solutions for mental health and rehabilitation as a growing focus in medical practice.

Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation

OBJECTIVE

Motor imagery-based brain-computer interface (MI-BCI) is a promising novel mode of stroke rehabilitation. The current study aims to investigate the feasibility of MI-BCI in upper limb rehabilitation of chronic stroke survivors and also to study the early event-related desynchronization after MI-BCI intervention.

METHODS

Changes in the characteristics of sensorimotor rhythm modulations in response to a short brain-computer interface (BCI) intervention for upper limb rehabilitation of stroke-disabled hand and normal hand were examined. The participants were trained to modulate their brain rhythms through motor imagery or execution during calibration, and they played a virtual marble game during the feedback session, where the movement of the marble was controlled by their sensorimotor rhythm.

RESULTS

Ipsilesional and contralesional activities were observed in the brain during the upper limb rehabilitation using BCI intervention. All the participants were able to successfully control the position of the virtual marble using their sensorimotor rhythm.

CONCLUSIONS

The preliminary results support the feasibility of BCI in upper limb rehabilitation and unveil the capability of MI-BCI as a promising medical intervention. This study provides a strong platform for clinicians to build upon new strategies for stroke rehabilitation by integrating MI-BCI with various therapeutic options to induce neural plasticity and recovery.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy

In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.

Exploring new horizons: Emerging therapeutic strategies for pediatric stroke

Pediatric stroke presents unique challenges, and optimizing treatment strategies is essential for improving outcomes in this vulnerable population. This review aims to provide an overview of new, innovative, and potential treatments for pediatric stroke, with a primary objective to stimulate further research in this field. Our review highlights several promising approaches in the realm of pediatric stroke management, including but not limited to stem cell therapy and robotic rehabilitation. These innovative interventions offer new avenues for enhancing functional recovery, reducing long-term disability, and tailoring treatments to individual patient needs. The findings of this review underscore the importance of ongoing research and development of innovative treatments in pediatric stroke. These advancements hold significant clinical relevance, offering the potential to improve the lives of children affected by stroke by enhancing the precision, efficacy, and accessibility of therapeutic interventions. Embracing these innovations is essential in our pursuit of better outcomes and a brighter future for pediatric stroke care.

A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study

Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 ± 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 ± 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.

Brain-computer interface digital prescription for neurological disorders

Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain-computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close-looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies. The third part summarizes previous examples of intervention, focusing on how to select appropriate interaction paradigms for patients with different functional impairments. For the last part, we discussed the indicators and influencing factors in evaluating the therapeutic effect of BCI as intervention.

Brain-computer interface in critical care and rehabilitation

This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.

Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial

BACKGROUND

Restorative Brain-Computer Interfaces (BCI) that combine motor imagery with visual feedback and functional electrical stimulation (FES) may offer much-needed treatment alternatives for patients with severely impaired upper limb (UL) function after a stroke.

OBJECTIVES

This study aimed to examine if BCI-based training, combining motor imagery with FES targeting finger/wrist extensors, is more effective in improving severely impaired UL motor function than conventional therapy in the subacute phase after stroke, and if patients with preserved cortical-spinal tract (CST) integrity benefit more from BCI training.

METHODS

Forty patients with severe UL paresis (< 13 on Action Research Arm Test (ARAT) were randomized to either a 12-session BCI training as part of their rehabilitation or conventional UL rehabilitation. BCI sessions were conducted 3-4 times weekly for 3-4 weeks. At baseline, Transcranial Magnetic Stimulation (TMS) was performed to examine CST integrity. The main endpoint was the ARAT at 3 months post-stroke. A binominal logistic regression was conducted to examine the effect of treatment group and CST integrity on achieving meaningful improvement. In the BCI group, electroencephalographic (EEG) data were analyzed to investigate changes in event-related desynchronization (ERD) during the course of therapy.

RESULTS

Data from 35 patients (15 in the BCI group and 20 in the control group) were analyzed at 3-month follow-up. Few patients (10/35) improved above the minimally clinically important difference of 6 points on ARAT, 5/15 in the BCI group, 5/20 in control. An independent-samples Mann-Whitney U test revealed no differences between the two groups, p = 0.382. In the logistic regression only CST integrity was a significant predictor for improving UL motor function, p = 0.007. The EEG analysis showed significant changes in ERD of the affected hemisphere and its lateralization only during unaffected UL motor imagery at the end of the therapy.

CONCLUSION

This is the first RCT examining BCI training in the subacute phase where only patients with severe UL paresis were included. Though more patients in the BCI group improved relative to the group size, the difference between the groups was not significant. In the present study, preserved CTS integrity was much more vital for UL improvement than which type of intervention the patients received. Larger studies including only patients with some preserved CST integrity should be attempted.

Community mobility and participation assessment of manual wheelchair users: a review of current techniques and challenges

According to the World Health Organization, hundreds of individuals commence wheelchair use daily, often due to an injury such as spinal cord injury or through a condition such as a stroke. However, manual wheelchair users typically experience reductions in individual community mobility and participation. In this review, articles from 2017 to 2023 were reviewed to identify means of measuring community mobility and participation of manual wheelchair users, factors that can impact these aspects, and current rehabilitation techniques for improving them. The selected articles document current best practices utilizing self-surveys, in-clinic assessments, and remote tracking through GPS and accelerometer data, which rehabilitation specialists can apply to track their patients' community mobility and participation accurately. Furthermore, rehabilitation methods such as wheelchair training programs, brain-computer interface triggered functional electric stimulation therapy, and community-based rehabilitation programs show potential to improve the community mobility and participation of manual wheelchair users. Recommendations were made to highlight potential avenues for future research.

Progress in neurorehabilitation research and the support by the National Natural Science Foundation of China from 2010 to 2022

The National Natural Science Foundation of China is one of the major funding agencies for neurorehabilitation research in China. This study reviews the frontier directions and achievements in the field of neurorehabilitation in China and worldwide. We used data from the Web of Science Core Collection (WoSCC) database to analyze the publications and data provided by the National Natural Science Foundation of China to analyze funding information. In addition, the prospects for neurorehabilitation research in China are discussed. From 2010 to 2022, a total of 74,220 publications in neurorehabilitation were identified, with there being an overall upward tendency. During this period, the National Natural Science Foundation of China has funded 476 research projects with a total funding of 192.38 million RMB to support neurorehabilitation research in China. With the support of the National Natural Science Foundation of China, China has made some achievements in neurorehabilitation research. Research related to neurorehabilitation is believed to be making steady and significant progress in China.

New approaches to recovery after stroke

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.

The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review

PURPOSE

Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to evaluate the clinical effects of BCI-robot systems.

METHODS

The articles published from January 2010 to December 2020 have been searched by using the databases (EMBASE, PubMed, CINAHL, EBSCO, Web of Science and manual search). The single-group studies were qualitatively described, and only the controlled-trial studies were included for the meta-analysis. The mean difference (MD) of Fugl-Meyer Assessment (FMA) scores were pooled and the random-effects model method was used to perform the meta-analysis. The PRISMA criteria were followed in current review.

RESULTS

A total of 897 records were identified, eight single-group studies and 11 controlled-trial studies were included in our review. The systematic analysis indicated that the BCI-robot systems had a significant improvement on motor function recovery. The meta-analysis showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects (p > 0.05).

CONCLUSION

The use of BCI-robot systems has significant improvement on the motor function recovery of hemiparetic upper-limb, and there is a sustaining effect. The meta-analysis showed no statistical difference between the experimental group (BCI-robot) and the control group (robot). However, there are a few shortcomings in the experimental design of existing studies, more clinical trials need to be conducted, and the experimental design needs to be more rigorous.Implications for RehabilitationIn this review, we evaluated the clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation. After we screened the databases, 19 articles were included in this review. These articles all clinical trial research, they all used non-invasive brain-computer interfaces and upper-limb robot.We conducted the systematic review with nine articles, the result indicated that the BCI-robot system had a significant improvement on motor function recovery. Eleven articles were included for the meta-analysis, the result showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects.We thought the result of meta-analysis which showed no statistic difference was probably caused by the heterogenicity of clinical trial designs of these articles.We thought the BCI-robot systems are promising strategies for post-stroke rehabilitation. And we gave several suggestions for further research: (1) The experimental design should be more rigorous, and describe the experimental designs in detail, especially the control group intervention, to make the experiment replicability. (2) New evaluation criteria need to be established, more objective assessment such as biomechanical assessment, fMRI should be utilised as the primary outcome. (3) More clinical studies with larger sample size, novel external devices, and BCI systems need to be conducted to investigate the differences between BCI-robot system and other interventions. (4) Further research could shift the focus to the patients who are in subacute stage, to explore if the early BCI training can make a positive impact on cerebral cortical recovery.

Efficacy of brain-computer interfaces on upper extremity motor function rehabilitation after stroke: A systematic review and meta-analysis

BACKGROUND

The recovery of upper limb function is crucial to the daily life activities of stroke patients. Brain-computer interface technology may have potential benefits in treating upper limb dysfunction.

OBJECTIVE

To systematically evaluate the efficacy of brain-computer interfaces (BCI) in the rehabilitation of upper limb motor function in stroke patients.

METHODS

Six databases up to July 2023 were reviewed according to the PRSIMA guidelines. Randomized controlled trials of BCI-based upper limb functional rehabilitation for stroke patients were selected for meta-analysis by pooling standardized mean difference (SMD) to summarize the evidence. The Cochrane risk of bias tool was used to assess the methodological quality of the included studies.

RESULTS

Twenty-five studies were included. The studies showed that BCI had a small effect on the improvement of upper limb function after the intervention. In terms of total duration of training, < 12 hours of training may result in better rehabilitation, but training duration greater than 12 hours suggests a non significant therapeutic effect of BCI training.

CONCLUSION

This meta-analysis suggests that BCI has a slight efficacy in improving upper limb function and has favorable long-term outcomes. In terms of total duration of training, < 12 hours of training may lead to better rehabilitation.

MEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?

Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called [Formula: see text] to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. [Formula: see text] has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm.

A systematic review on functional electrical stimulation based rehabilitation systems for upper limb post-stroke recovery

Background

Stroke is one of the most common neurological conditions that often leads to upper limb motor impairments, significantly affecting individuals' quality of life. Rehabilitation strategies are crucial in facilitating post-stroke recovery and improving functional independence. Functional Electrical Stimulation (FES) systems have emerged as promising upper limb rehabilitation tools, offering innovative neuromuscular reeducation approaches.

Objective

The main objective of this paper is to provide a comprehensive systematic review of the start-of-the-art functional electrical stimulation (FES) systems for upper limb neurorehabilitation in post-stroke therapy. More specifically, this paper aims to review different types of FES systems, their feasibility testing, or randomized control trials (RCT) studies.

Methods

The FES systems classification is based on the involvement of patient feedback within the FES control, which mainly includes "Open-Loop FES Systems" (manually controlled) and "Closed-Loop FES Systems" (brain-computer interface-BCI and electromyography-EMG controlled). Thus, valuable insights are presented into the technological advantages and effectiveness of Manual FES, EEG-FES, and EMG-FES systems.

Results and discussion

The review analyzed 25 studies and found that the use of FES-based rehabilitation systems resulted in favorable outcomes for the stroke recovery of upper limb functional movements, as measured by the FMA (Fugl-Meyer Assessment) (Manually controlled FES: mean difference = 5.6, 95% CI (3.77, 7.5), P < 0.001; BCI-controlled FES: mean difference = 5.37, 95% CI (4.2, 6.6), P < 0.001; EMG-controlled FES: mean difference = 14.14, 95% CI (11.72, 16.6), P < 0.001) and ARAT (Action Research Arm Test) (EMG-controlled FES: mean difference = 11.9, 95% CI (8.8, 14.9), P < 0.001) scores. Furthermore, the shortcomings, clinical considerations, comparison to non-FES systems, design improvements, and possible future implications are also discussed for improving stroke rehabilitation systems and advancing post-stroke recovery. Thus, summarizing the existing literature, this review paper can help researchers identify areas for further investigation. This can lead to formulating research questions and developing new studies aimed at improving FES systems and their outcomes in upper limb rehabilitation.

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain's state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.

A comprehensive guide to BCI-based stroke neurorehabilitation interventions

Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation. Applicable regardless of infrastructure and study design limitations, this guide acts as a comprehensive reference for executing BCI-based stroke interventions. Furthermore, it encapsulates insights gleaned from administering hundreds of BCI rehabilitation sessions to stroke patients.•Presents a comprehensive methodology for implementing BCI-based upper extremity therapy in stroke patients.•Provides detailed guidance on the number of sessions, trials, as well as the necessary hardware and software for effective intervention.

Relationship between resting-state functional connectivity and change in motor function after motor imagery intervention in patients with stroke: a scoping review

BACKGROUND

In clinical practice, motor imagery has been proposed as a treatment modality for stroke owing to its feasibility in patients with severe motor impairment. Motor imagery-based interventions can be categorized as open- or closed-loop. Closed-loop intervention is based on voluntary motor imagery and induced peripheral sensory afferent (e.g., Brain Computer Interface (BCI)-based interventions). Meanwhile, open-loop interventions include methods without voluntary motor imagery or sensory afferent. Resting-state functional connectivity (rs-FC) is defined as a significant temporal correlated signal among functionally related brain regions without any stimulus. rs-FC is a powerful tool for exploring the baseline characteristics of brain connectivity. Previous studies reported changes in rs-FC after motor imagery interventions. Systematic reviews also reported the effects of motor imagery-based interventions at the behavioral level. This study aimed to review and describe the relationship between the improvement in motor function and changes in rs-FC after motor imagery in patients with stroke.

REVIEW PROCESS

The literature review was based on Arksey and O'Malley's framework. PubMed, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched up to September 30, 2023. The included studies covered the following topics: illusion without voluntary action, motor imagery, action imitation, and BCI-based interventions. The correlation between rs-FC and motor function before and after the intervention was analyzed. After screening by two independent researchers, 13 studies on BCI-based intervention, motor imagery intervention, and kinesthetic illusion induced by visual stimulation therapy were included.

CONCLUSION

All studies relating to motor imagery in this review reported improvement in motor function post-intervention. Furthermore, all those studies demonstrated a significant relationship between the change in motor function and rs-FC (e.g., sensorimotor network and parietal cortex).

Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.

Advances in Stroke Neurorehabilitation

Stroke is one of the leading causes of disability worldwide despite recent advances in hyperacute interventions to lessen the initial impact of stroke. Stroke recovery therapies are crucial in reducing the long-term disability burden after stroke. Stroke recovery treatment options have rapidly expanded within the last decade, and we are in the dawn of an exciting era of multimodal therapeutic approaches to improve post-stroke recovery. In this narrative review, we highlighted various promising advances in treatment and technologies targeting stroke rehabilitation, including activity-based therapies, non-invasive and minimally invasive brain stimulation techniques, robotics-assisted therapies, brain-computer interfaces, pharmacological treatments, and cognitive therapies. These new therapies are targeted to enhance neural plasticity as well as provide an adequate dose of rehabilitation and improve adherence and participation. Novel activity-based therapies and telerehabilitation are promising tools to improve accessibility and provide adequate dosing. Multidisciplinary treatment models are crucial for post-stroke neurorehabilitation, and further adjuvant treatments with brain stimulation techniques and pharmacological agents should be considered to maximize the recovery. Among many challenges in the field, the heterogeneity of patients included in the study and the mixed methodologies and results across small-scale studies are the cardinal ones. Biomarker-driven individualized approaches will move the field forward, and so will large-scale clinical trials with a well-targeted patient population.

Effects of brain-Computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial

Aim

To explore the effects of brain-computer interface training combined with mindfulness therapy on Hemiplegic Patients with Stroke.

Background

The prevention and treatment of stroke still faces great challenges. Maximizing the improvement of patients' ability to perform activities of daily living, limb motor function, and reducing anxiety, depression, and other social and psychological problems to improve patients' overall quality of life is the focus and difficulty of clinical rehabilitation work.

Methods

Patients were recruited from December 2021 to November 2022, and assigned to either the intervention or control group following a simple randomization procedure (computer-generated random numbers). Both groups received conventional rehabilitation treatment, while patients in the intervention group additionally received brain-computer interface training and mindfulness therapy. The continuous treatment duration was 5 days per week for 8 weeks. Limb motor function, activities of daily living, mindfulness attention awareness level, sleep quality, and quality of life of the patients were measured (in T0, T1, and T2). Generalized estimated equation (GEE) were used to evaluate the effects. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300070382).

Results

A total of 128 participants were randomized and 64 each were assigned to the intervention and control groups (of these, eight patients were lost to follow-up). At 6 months, compared with the control group, intervention group showed statistically significant improvements in limb motor function, mindful attention awareness, activities of daily living, sleep quality, and quality of life.

Conclusion

Brain-computer interface combined with mindfulness therapy training can improve limb motor function, activities of daily living, mindful attention awareness, sleep quality, and quality of life in hemiplegic patients with stroke.

Impact

This study provides valuable insights into post-stroke care. It may help improve the effect of rehabilitation nursing to improve the comprehensive ability and quality of life of patients after stroke.

Clinical review registration

https://www.chictr.org.cn/, identifier ChiCTR2300070382.

Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy

Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.

Brain activation by a VR-based motor imagery and observation task: An fMRI study

Training motor imagery (MI) and motor observation (MO) tasks is being intensively exploited to promote brain plasticity in the context of post-stroke rehabilitation strategies. This may benefit from the use of closed-loop neurofeedback, embedded in brain-computer interfaces (BCI's) to provide an alternative non-muscular channel, which may be further augmented through embodied feedback delivered through virtual reality (VR). Here, we used functional magnetic resonance imaging (fMRI) in a group of healthy adults to map brain activation elicited by an ecologically-valid task based on a VR-BCI paradigm called NeuRow, whereby participants perform MI of rowing with the left or right arm (i.e., MI), while observing the corresponding movement of the virtual arm of an avatar (i.e., MO), on the same side, in a first-person perspective. We found that this MI-MO task elicited stronger brain activation when compared with a conventional MI-only task based on the Graz BCI paradigm, as well as to an overt motor execution task. It recruited large portions of the parietal and occipital cortices in addition to the somatomotor and premotor cortices, including the mirror neuron system (MNS), associated with action observation, as well as visual areas related with visual attention and motion processing. Overall, our findings suggest that the virtual representation of the arms in an ecologically-valid MI-MO task engage the brain beyond conventional MI tasks, which we propose could be explored for more effective neurorehabilitation protocols.

Challenges of neural interfaces for stroke motor rehabilitation

More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.

Exploring the transformative influence of neuroplasticity on stroke rehabilitation: a narrative review of current evidence

This review aims to assess the role of neuroplasticity in facilitating stroke recovery and identify the challenges and limitations associated with its implementation. A comprehensive literature search was conducted to identify relevant studies, which were meticulously evaluated to determine the potential solutions for effectively harnessing neuroplasticity. The results indicate that neuroplasticity holds significant promise in stroke rehabilitation; however, individual variability in response to interventions, timing and duration of interventions and sociocultural and clinical factors pose challenges. Tailoring interventions to individual patient characteristics is crucial for optimising the impact of neuroplasticity. Despite challenges and limitations, the transformative potential of neuroplasticity in stroke rehabilitation is undeniable. The abstract concludes by emphasising the importance of a comprehensive understanding of individual variability, optimising intervention timing and duration and considering sociocultural and clinical factors. Future research and clinical practice should prioritise personalised interventions and interdisciplinary collaborations to fully exploit the vast potential of neuroplasticity in stroke recovery.

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

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.

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

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.

Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs

Introduction and objective

Video games are crucial to the entertainment industry, nonetheless they can be challenging to access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have the potential to help these individuals by allowing them to control video games using their brain signals. Furthermore, multiplayer BCI-based video games may provide valuable insights into how competitiveness or motivation affects the control of these interfaces. Despite the recent advancement in the development of code-modulated visual evoked potentials (c-VEPs) as control signals for high-performance BCIs, to the best of our knowledge, no studies have been conducted to develop a BCI-driven video game utilizing c-VEPs. However, c-VEPs could enhance user experience as an alternative method. Thus, the main goal of this work was to design, develop, and evaluate a version of the well-known 'Connect 4' video game using a c-VEP-based BCI, allowing 2 users to compete by aligning 4 same-colored coins vertically, horizontally or diagonally.

Methods

The proposed application consists of a multiplayer video game controlled by a real-time BCI system processing 2 electroencephalograms (EEGs) sequentially. To detect user intention, columns in which the coin can be placed was encoded with shifted versions of a pseudorandom binary code, following a traditional circular shifting c-VEP paradigm. To analyze the usability of our application, the experimental protocol comprised an evaluation session by 22 healthy users. Firstly, each user had to perform individual tasks. Afterward, users were matched and the application was used in competitive mode. This was done to assess the accuracy and speed of selection. On the other hand, qualitative data on satisfaction and usability were collected through questionnaires.

Results

The average accuracy achieved was 93.74% ± 1.71%, using 5.25 seconds per selection. The questionnaires showed that users felt a minimal workload. Likewise, high satisfaction values were obtained, highlighting that the application was intuitive and responds quickly and smoothly.

Conclusions

This c-VEP based multiplayer video game has reached suitable performance on 22 users, supported by high motivation and minimal workload. Consequently, compared to other versions of "Connect 4" that utilized different control signals, this version has exhibited superior performance.

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

Introduction

Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer.

Methods

Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement.

Results

A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon.

Discussion

We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.

Virtual reality and motor imagery for early post-stroke rehabilitation

BACKGROUND

Motor impairment is a common consequence of stroke causing difficulty in independent movement. The first month of post-stroke rehabilitation is the most effective period for recovery. Movement imagination, known as motor imagery, in combination with virtual reality may provide a way for stroke patients with severe motor disabilities to begin rehabilitation.

METHODS

The aim of this study is to verify whether motor imagery and virtual reality help to activate stroke patients' motor cortex. 16 acute/subacute (< 6 months) stroke patients participated in this study. All participants performed motor imagery of basketball shooting which involved the following tasks: listening to audio instruction only, watching a basketball shooting animation in 3D with audio, and also performing motor imagery afterwards. Electroencephalogram (EEG) was recorded for analysis of motor-related features of the brain such as power spectral analysis in the [Formula: see text] and [Formula: see text] frequency bands and spectral entropy. 18 EEG channels over the motor cortex were used for all stroke patients.

RESULTS

All results are normalised relative to all tasks for each participant. The power spectral densities peak near the [Formula: see text] band for all participants and also the [Formula: see text] band for some participants. Tasks with instructions during motor imagery generally show greater power spectral peaks. The p-values of the Wilcoxon signed-rank test for band power comparison from the 18 EEG channels between different pairs of tasks show a 0.01 significance of rejecting the band powers being the same for most tasks done by stroke subjects. The motor cortex of most stroke patients is more active when virtual reality is involved during motor imagery as indicated by their respective scalp maps of band power and spectral entropy.

CONCLUSION

The resulting activation of stroke patient's motor cortices in this study reveals evidence that it is induced by imagination of movement and virtual reality supports motor imagery. The framework of the current study also provides an efficient way to investigate motor imagery and virtual reality during post-stroke rehabilitation.

Acceptability of a Telerehabilitation Biofeedback System Among Stroke Survivors: A Qualitative Analysis

Electromyography (EMG) biofeedback delivered via telerehabilitation can increase access to occupational therapy services for stroke survivors with severe impairment, but there is limited research on its acceptability. This study identified factors influencing the acceptability of a complex, muscle biofeedback system (Tele-REINVENT) for upper extremity sensorimotor stroke telerehabilitation among stroke survivors. We conducted interviews with stroke survivors (n = 4) who used Tele-REINVENT at home for 6 weeks and analyzed the data with reflexive thematic analysis. Biofeedback, customization, gamification, and predictability affected the acceptability of Tele-REINVENT among stroke survivors. Across themes, features and experiences that gave participants agency and control were more acceptable. Our findings contribute to the design and development of at-home EMG biofeedback interventions, which can improve access to advanced occupational therapy treatment options for those who need it most.

High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing

Real-time functional imaging of human neural activity and its closed-loop feedback enable voluntary control of targeted brain regions. In particular, a brain-computer interface (BCI), a direct bridge of neural activities and machine actuation is one promising clinical application of neurofeedback. Although a variety of studies reported successful self-regulation of motor cortical activities probed by scalp electroencephalogram (EEG), it remains unclear how neurophysiological, experimental conditions or BCI designs influence variability in BCI learning. Here, we provide the EEG data during using BCIs based on sensorimotor rhythm (SMR), consisting of 4 separate datasets. All EEG data were acquired with a high-density scalp EEG setup containing 128 channels covering the whole head. All participants were instructed to perform motor imagery of right-hand movement as the strategy to control BCIs based on the task-related power attenuation of SMR magnitude, that is event-related desynchronization. This dataset would allow researchers to explore the potential source of variability in BCI learning efficiency and facilitate follow-up studies to test the explicit hypotheses explored by the dataset.

Activation of inflammasomes and their effects on neuroinflammation at the microelectrode-tissue interface in intracortical implants

Invasive neuroprosthetics rely on microelectrodes (MEs) to record or stimulate the activity of large neuron assemblies. However, MEs are subjected to tissue reactivity in the central nervous system (CNS) due to the foreign body response (FBR) that contribute to chronic neuroinflammation and ultimately result in ME failure. An endogenous, acute set of mechanisms responsible for the recognition and targeting of foreign objects, called the innate immune response, immediately follows the ME implant-induced trauma. Inflammasomes are multiprotein structures that play a critical role in the initiation of an innate immune response following CNS injuries. The activation of inflammasomes facilitates a range of innate immune response cascades and results in neuroinflammation and programmed cell death. Despite our current understanding of inflammasomes, their roles in the context of neural device implantation remain unknown. In this study, we implanted a non-functional Utah electrode array (UEA) into the rat somatosensory cortex and studied the inflammasome signaling and the corresponding downstream effects on inflammatory cytokine expression and the inflammasome-mediated cell death mechanism of pyroptosis. Our results not only demonstrate the continuous activation of inflammasomes and their contribution to neuroinflammation at the electrode-tissue interface but also reveal the therapeutic potential of targeting inflammasomes to attenuate the FBR in invasive neuroprosthetics.

Mechanical digit sensory stimulation: a randomized control trial on neurological and motor recovery in acute stroke

Background

Mechanical digit sensory stimulation (MDSS) is a novel therapy designed to accelerate the recovery of upper limb (including hand) function in patients with hemiplegia following a stroke. The primary goal of this study was to investigate the effect of MDSS on patients with acute ischemic stroke (AIS).

Methods

Sixty-one inpatients with AIS were randomly divided into conventional rehabilitation group (RG) and stimulation group (SG), and the latter group received MDSS therapy. A healthy group consisting of 30 healthy adults was also included. The interleukin-17A (IL-17A), vascular endothelial growth factor A (VEGF-A), and tumor necrosis factor-alpha (TNF-α) plasma levels were measured in all subjects. The neurological and motor functions of patients were evaluated using the National Institutes of Health Stroke Scale (NIHSS), Mini-Mental State Examination (MMSE), Fugel-Meyer Assessment (FMA), and Modified Barthel Index (MBI).

Results

After 12 days of intervention, the IL-17A, TNF-α, and NIHSS levels were significantly decreased, while the VEGF-A, MMSE, FMA, and MBI levels were significantly increased in both disease groups. No significant difference was observed between both disease groups after intervention. The levels of IL-17A and TNF-α were positively correlated with NIHSS but negatively correlated with MMSE, FMA, and MBI. The VEGF-A levels were negatively correlated with NIHSS but positively correlated with MMSE, FMA, and MBI.

Conclusion

Both MDSS and conventional rehabilitation significantly reduce the production of IL-17A and TNF-α, increase the VEGF-A levels, and effectively improve cognition and motor function of hemiplegic patients with AIS, and the effects of MDSS and conventional rehabilitation are comparable.

Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study

Background

Brain-computer interfaces (BCIs) have been proven to be effective for hand motor recovery after stroke. Facing kinds of dysfunction of the paretic hand, the motor task of BCIs for hand rehabilitation is relatively single, and the operation of many BCI devices is complex for clinical use. Therefore, we proposed a functional-oriented, portable BCI equipment and explored the efficiency of hand motor recovery after a stroke.

Materials and methods

Stroke patients were randomly assigned to the BCI group and the control group. The BCI group received BCI-based grasp/open motor training, while the control group received task-oriented guidance training. Both groups received 20 sessions of motor training in 4 weeks, and each session lasted for 30 min. The Fugl-Meyer assessment of the upper limb (FMA-UE) was applied for the assessment of rehabilitation outcomes, and the EEG signals were obtained for processing.

Results

The progress of FMA-UE between the BCI group [10.50 (5.75, 16.50)] and the control group [5.00 (4.00, 8.00)] was significantly different (Z = -2.834, P = 0.005). Meanwhile, the FMA-UE of both groups improved significantly (P < 0.001). A total of 24 patients in the BCI group achieved the minimal clinically important difference (MCID) of FMA-UE with an effective rate of 80%, and 16 in the control group achieved the MCID, with an effective rate of 51.6%. The lateral index of the open task in the BCI group was significantly decreased (Z = -2.704, P = 0.007). The average BCI accuracy for 24 stroke patients in 20 sessions was 70.7%, which was improved by 5.0% in the final session compared with the first session.

Conclusion

Targeted hand movement and two motor task modes, namely grasp and open, to be applied in a BCI design may be suitable in stroke patients with hand dysfunction. The functional-oriented, portable BCI training can promote hand recovery after a stroke, and it is expected to be widely used in clinical practice. The lateral index change of inter-hemispheric balance may be the mechanism of motor recovery.

Trial registration number

ChiCTR2100044492.

Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study

BACKGROUND AND AIMS

Brain-computer interfaces (BCIs) are emerging as a promising tool for upper limb recovery after stroke, and motor tasks are an essential part of BCIs for patient training and control of rehabilitative/assistive BCIs. However, the correlation between brain activation with different levels of motor impairment and motor tasks in BCIs is still not so clear. Thus, we aim to compare the brain activation of different levels of motor impairment in performing the hand grasping and opening tasks in BCIs.

METHODS

We instructed stroke patients to perform motor attempts (MA) to grasp and open the affected hand for 30 trials, respectively. During this period, they underwent EEG acquisition and BCIs accuracy recordings. They also received detailed history records and behavioral scale assessments (the Fugl-Meyer assessment of upper limb, FMA-UE).

RESULTS

The FMA-UE was negatively correlated with the event-related desynchronization (ERD) of the affected hemisphere during open MA (R = - 0.423, P = 0.009) but not with grasp MA (R = - 0.058, P = 0.733). Then we divided the stroke patients into group 1 (Brunnstrom recovery stages between I to II, n = 19) and group 2 (Brunnstrom recovery stages between III to VI, n = 23). No difference during the grasping task (t = 0.091, P = 0.928), but a significant difference during the open task (t = 2.156, P = 0.037) was found between the two groups on the affected hemisphere. No significant difference was found in the unaffected hemisphere.

CONCLUSIONS

The study indicated that brain activation is positively correlated with the hand function of stroke in open-hand tasks. In the grasping task, the patients in the different groups have a similar brain response, while in the open task, mildly injured patients have more brain activation in open the hand than the poor hand function patients.

Wireless EEG: A survey of systems and studies

The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important-sometimes difficult-to-obtain-features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.

Investigating the impact of visual perspective in a motor imagery-based brain-robot interaction: A pilot study with healthy participants

Introduction

Motor Imagery (MI)-based Brain Computer Interfaces (BCI) have raised gained attention for their use in rehabilitation therapies since they allow controlling an external device by using brain activity, in this way promoting brain plasticity mechanisms that could lead to motor recovery. Specifically, rehabilitation robotics can provide precision and consistency for movement exercises, while embodied robotics could provide sensory feedback that can help patients improve their motor skills and coordination. However, it is still not clear whether different types of visual feedback may affect the elicited brain response and hence the effectiveness of MI-BCI for rehabilitation.

Methods

In this paper, we compare two visual feedback strategies based on controlling the movement of robotic arms through a MI-BCI system: 1) first-person perspective, with visual information that the user receives when they view the robot arms from their own perspective; and 2) third-person perspective, whereby the subjects observe the robot from an external perspective. We studied 10 healthy subjects over three consecutive sessions. The electroencephalographic (EEG) signals were recorded and evaluated in terms of the power of the sensorimotor rhythms, as well as their lateralization, and spatial distribution.

Results

Our results show that both feedback perspectives can elicit motor-related brain responses, but without any significant differences between them. Moreover, the evoked responses remained consistent across all sessions, showing no significant differences between the first and the last session.

Discussion

Overall, these results suggest that the type of perspective may not influence the brain responses during a MI- BCI task based on a robotic feedback, although, due to the limited sample size, more evidence is required. Finally, this study resulted into the production of 180 labeled MI EEG datasets, publicly available for research purposes.

BCI-activated electrical stimulation in children with perinatal stroke and hemiparesis: A pilot study

Background

Perinatal stroke (PS) causes most hemiparetic cerebral palsy (CP) and results in lifelong disability. Children with severe hemiparesis have limited rehabilitation options. Brain computer interface- activated functional electrical stimulation (BCI-FES) of target muscles may enhance upper extremity function in hemiparetic adults. We conducted a pilot clinical trial to assess the safety and feasibility of BCI-FES in children with hemiparetic CP.

Methods

Thirteen participants (mean age = 12.2 years, 31% female) were recruited from a population-based cohort. Inclusion criteria were: (1) MRI-confirmed PS, (2) disabling hemiparetic CP, (3) age 6–18 years, (4) informed consent/assent. Those with neurological comorbidities or unstable epilepsy were excluded. Participants attended two BCI sessions: training and rehabilitation. They wore an EEG-BCI headset and two forearm extensor stimulation electrodes. Participants’ imagination of wrist extension was classified on EEG, after which muscle stimulation and visual feedback were provided when the correct visualization was detected.

Results

No serious adverse events or dropouts occurred. The most common complaints were mild headache, headset discomfort and muscle fatigue. Children ranked the experience as comparable to a long car ride and none reported as unpleasant. Sessions lasted a mean of 87 min with 33 min of stimulation delivered. Mean classification accuracies were (M = 78.78%, SD = 9.97) for training and (M = 73.48, SD = 12.41) for rehabilitation. Mean Cohen’s Kappa across rehabilitation trials was M = 0.43, SD = 0.29, range = 0.019–1.00, suggesting BCI competency.

Conclusion

Brain computer interface-FES was well -tolerated and feasible in children with hemiparesis. This paves the way for clinical trials to optimize approaches and test efficacy.