SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation

Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: a meta-analysis

BACKGROUND

Previous research has used the brain-computer interface (BCI) to promote upper-limb motor rehabilitation. However, the results of these studies were variable, leaving efficacy unclear.

OBJECTIVES

This review aims to evaluate the effects of BCI-based training on post-stroke upper-limb rehabilitation and identify potential factors that may affect the outcome.

DESIGN

A meta-analysis including all available randomized-controlled clinical trials (RCTs) that reported the efficacy of BCI-based training on upper-limb motor rehabilitation after stroke.

DATA SOURCES AND METHODS

We searched PubMed, Cochrane Library, and Web of Science before September 15, 2024, for relevant studies. The primary efficacy outcome was the Fugl-Meyer Assessment-Upper extremity (FMA-UE). RevMan 5.4.1 with a random effect model was used for data synthesis and analysis. Mean difference (MD) and 95% confidence interval (95%CI) were calculated.

RESULTS

Twenty-one RCTs (n = 886 patients) were reviewed in the meta-analysis. Compared with control, BCI-based training exerted significant effects on FMA-UE (MD = 3.69, 95%CI 2.41-4.96, P < 0.00001, moderate-quality evidence), Wolf Motor Function Test (WMFT) (MD = 5.00, 95%CI 2.14-7.86, P = 0.0006, low-quality evidence), and Action Research Arm Test (ARAT) (MD = 2.04, 95%CI 0.25-3.82, P = 0.03, high-quality evidence). Additionally, BCI-based training was effective on FMA-UE for both subacute (MD = 4.24, 95%CI 1.81-6.67, P = 0.0006) and chronic patients (MD = 2.63, 95%CI 1.50-3.76, P < 0.00001). BCI combined with functional electrical stimulation (FES) (MD = 4.37, 95%CI 3.09-5.65, P < 0.00001), robots (MD = 2.87, 95%CI 0.69-5.04, P = 0.010), and visual feedback (MD = 4.46, 95%CI 0.24-8.68, P = 0.04) exhibited significant effects on FMA-UE. BCI combined with FES significantly improved FMA-UE for both subacute (MD = 5.31, 95%CI 2.58-8.03, P = 0.0001) and chronic patients (MD = 3.71, 95%CI 2.44-4.98, P < 0.00001), and BCI combined with robots was effective for chronic patients (MD = 1.60, 95%CI 0.15-3.05, P = 0.03). Better results may be achieved with daily training sessions ranging from 20 to 90 min, conducted 2-5 sessions per week for 3-4 weeks.

CONCLUSIONS

BCI-based training may be a reliable rehabilitation program to improve upper-limb motor impairment and function.

TRIAL REGISTRATION

PROSPERO registration ID: CRD42022383390.

Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis

BACKGROUND

In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.

NEW METHOD

With extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix.

RESULTS

The experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7 s, the average recognition accuracy of the subjects increases by 10.71 % on the Benchmark dataset and by 6.98 % on the BETA dataset, respectively.

COMPARISON WITH EXISTING METHODS

Our extended method outperforms the state-of-the-art methods by at least 3 %, and it effectively suppresses local noise and maintains excellent scalability.

CONCLUSIONS FOR RESEARCH ARTICLES

The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.

An fMRI study on the generalization of motor learning after brain actuated supernumerary robot training

Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network.

Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis

BACKGROUND

Robot-assisted upper-limb rehabilitation has been studied for many years, with many randomised controlled trials (RCTs) investigating the effects of robotic-assisted training on affected limbs. The current trend directs towards end-effector devices. However, most studies have focused on the effectiveness of rehabilitation devices, but studies on device sizes are relatively few.

GOAL

Systematically review the effect of a portable rehabilitation robot (PRR) on the rehabilitation effectiveness of paralysed upper limbs compared with non-robotic therapy.

METHODS

A meta-analysis was conducted on literature that included the Fugl-Meyer Assessment (FMA) obtained from the PubMed and Web of Science (WoS) electronic databases until June 2023.

RESULTS

A total of 9 studies, which included RCTs, were completed and a meta-analysis was conducted on 8 of them. The analysis involved 295 patients. The influence on upper-limb function before and after treatment in a clinical environment is analysed by comparing the experimental group using the portable upper-limb rehabilitation robot with the control group using conventional therapy. The result shows that portable robots prove to be effective (FMA: SMD = 0.696, 95% = 0.099 to.293, p < 0.05).

DISCUSSION

Both robot-assisted and conventional rehabilitation effects are comparable. In some studies, PRR performs better than conventional rehabilitation, but conventional treatments are still irreplaceable. Smaller size with better portability has its advantages, and portable upper-limb rehabilitation robots are feasible in clinical rehabilitation.

CONCLUSION

Although portable upper-limb rehabilitation robots are clinically beneficial, few studies have focused on portability. Further research should focus on modular design so that rehabilitation robots can be decomposed, which benefits remote rehabilitation and household applications.

Active, Actuated, and Assistive: a Scoping Review of Exoskeletons for the Hands and Wrists

BACKGROUND

Assistive technology is often incorporated into rehabilitation and support for those impacted by upper limb impairments. When powered, these devices provide additional force to the joints of users with muscle weakness. Actuated devices allow dynamic movement compared to splints, therefore improving the ability to complete activities of daily living. However, these devices are not often prescribed and are underrepresented in research and clinical settings.

OBJECTIVE

This review examined the existing literature on devices developed to support hand and wrist functionality in daily activities. Focusing on active, powered, and actuated devices, to gain a clearer understanding of the current limitations in their design and prescription.

METHODOLOGY

The scoping review was conducted using the PRISMA-ScR guidelines. A systematic search was done on MEDLINE, EMBASE, Scopus, Web of Science, and NHS the Knowledge Network from inception to May 2023. Articles were included if the device was portable; supported the hands and wrist actively using an actuator; and could be used for assistive living during or post-rehabilitation period.

FINDINGS

A total of 135 studies were included in the analysis of which 34 were clinical trials. The design and control methods of 121 devices were analyzed. Electrical stimulation and direct mechanical transmission were popular actuation methods. Electromyography (EMG) and joint movement detection were highly used control methods to translate user intentions to device actuation. A total of 226 validation methods were reported, of which 44% were clinically validated. Studies were often not conducted in operational environments with 69% at technology readiness levels ≤ 6, indicating that further development and testing is required.

CONCLUSION

The existing literature on hand and wrist exoskeletons presents large variations in validation methods and technical requirements for user-specific characteristics. This suggests a need for well-defined testing protocols and refined reporting of device designs. This would improve the significance of clinical outcomes and new assistive technology.

Design, modeling, and preliminary evaluation of a 3D-printed wrist-hand grasping orthosis for stroke survivors

Stroke causes neurological and physical impairment in millions of people around the world every year. To better comprehend the upper-limb needs and challenges stroke survivors face and the issues associated with existing technology and formulate ideas for a technological solution, the authors conversed with 153 members of the ecosystem (60 neuro patients, 30 caregivers, and 63 medical providers). Patients fell into two populations depending on their upper-limb impairment: spastic (stiff, clenched hands) and flaccid (limp hands). For this work, the authors chose to focus on the second category and developed a set of design constraints based on the information collected through customer discovery. With these in mind, they designed and prototyped a 3D-printed powered wrist-hand grasping orthosis (exoskeleton) to aid in recovery. The orthosis is easily custom-sized based on two parameters and derived anatomical relationships. The researchers tested the prototype on a survivor of stroke and modeled the kinematic behavior of the orthosis with and without load. The prototype neared or exceeded the target design constraints and was able to grasp objects consistently and stably, as well as exercise the patients' hands. In particular, donning time was only 42 s, as compared to the next fastest time of 3 min reported in literature. This device has the potential for effective neurorehabilitation in a home setting, and it lays the foundation for clinical trials and further device development.

Effect of Upper Robot-Assisted Training on Upper Limb Motor, Daily Life Activities, and Muscular Tone in Patients With Stroke: A Systematic Review and Meta-Analysis

BACKGROUND

Upper limb rehabilitation robot is a relatively new technology, but its effectiveness remains debatable due to the inconsistent results of clinical trials. This article intends to assess how upper limb rehabilitation robots help the functional recovery of stroke patients.

METHODS

PubMed, Embase, Cochrane Library, and Web of Science databases were searched for eligible studies to explore the effect of upper limb rehabilitation robots on upper limb motor function, muscle tone, and daily living activities.

RESULTS

Eighteen trials with 573 stroke patients met the inclusion criteria. The results showed that compared to conventional rehabilitation training, patients who received upper limb robotic therapy (RT) had significantly improved Fugl-Meyer Upper Extremity Motor Assessment (FMA-UE) scores (weighted mean differences [WMD]: 5.27, 95% confidence intervals [CI]: 3.36, 7.17), Action Research Arm Test (ARAT) scores (WMD: 4.07, 95% CI: -4.14, 12.28), Modified Barthel Index (MBI) scores (WMD: 9.55, 95% CI: 6.37, 12.73), and modified Ashworth Scale (MAS) scores (WMD: -0.28, 95% CI: -0.50, 0.06), with no significant heterogeneity.

CONCLUSIONS

Upper limb robot-assisted training is superior to conventional training in terms of improving upper limb motor impairment, ability to perform daily living activities, and muscle tone recovery, which supports the application of robots in clinical practice.

The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and meta-analysis

Introduction

Several clinical studies have demonstrated that brain-computer interfaces (BCIs) controlled functional electrical stimulation (FES) facilitate neurological recovery in patients with stroke. This review aims to evaluate the effectiveness of BCI-FES training on upper limb functional recovery in stroke patients.

Methods

PubMed, Embase, Cochrane Library, Science Direct and Web of Science were systematically searched from inception to October 2023. Randomized controlled trials (RCTs) employing BCI-FES training were included. The methodological quality of the RCTs was assessed using the PEDro scale. Meta-analysis was conducted using RevMan 5.4.1 and STATA 18.

Results

The meta-analysis comprised 290 patients from 10 RCTs. Results showed a moderate effect size in upper limb function recovery through BCI-FES training (SMD = 0.50, 95% CI: 0.26-0.73, I2 = 0%, p < 0.0001). Subgroup analysis revealed that BCI-FES training significantly enhanced upper limb motor function in BCI-FES vs. FES group (SMD = 0.37, 95% CI: 0.00-0.74, I2 = 21%, p = 0.05), and the BCI-FES + CR vs. CR group (SMD = 0.61, 95% CI: 0.28-0.95, I2 = 0%, p = 0.0003). Moreover, BCI-FES training demonstrated effectiveness in both subacute (SMD = 0.56, 95% CI: 0.25-0.87, I2 = 0%, p = 0.0004) and chronic groups (SMD = 0.42, 95% CI: 0.05-0.78, I2 = 45%, p = 0.02). Subgroup analysis showed that both adjusting (SMD = 0.55, 95% CI: 0.24-0.87, I2 = 0%, p = 0.0006) and fixing (SMD = 0.43, 95% CI: 0.07-0.78, I2 = 46%, p = 0.02). BCI thresholds before training significantly improved motor function in stroke patients. Both motor imagery (MI) (SMD = 0.41 95% CI: 0.12-0.71, I2 = 13%, p = 0.006) and action observation (AO) (SMD = 0.73, 95% CI: 0.26-1.20, I2 = 0%, p = 0.002) as mental tasks significantly improved upper limb function in stroke patients.

Discussion

BCI-FES has significant immediate effects on upper limb function in subacute and chronic stroke patients, but evidence for its long-term impact remains limited. Using AO as the mental task may be a more effective BCI-FES training strategy.

Systematic review registration

Identifier: CRD42023485744, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023485744.

Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review

Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.

Design and application of pneumatic rehabilitation glove system based on brain-computer interface

Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.

An Adaptive Hybrid Brain-Computer Interface for Hand Function Rehabilitation of Stroke Patients

Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and signal-to-noise ratio (SNR) of electroencephalograph (EEG), MI based BCI system that involves decoding hand movements within the same limb remains lower classification accuracy and poorer practicality. To overcome the limitations, an adaptive hybrid BCI system combining MI and steady-state visually evoked potential (SSVEP) is developed to improve decoding accuracy while enhancing neural engagement. On the one hand, the SSVEP evoked by visual stimuli based on action-state flickering coding approach significantly improves the recognition accuracy compared to the pure MI based BCI. On the other hand, to reduce the impact of SSVEP on MI due to the dual-task interference effect, the event-related desynchronization (ERD) based neural engagement is monitored and employed for feedback in real-time to ensure the effective execution of MI tasks. Eight healthy subjects and six post-stroke patients were recruited to verify the effectiveness of the system. The results showed that the four-class gesture recognition accuracies of healthy individuals and patients could be improved to 94.37 ± 4.77 % and 79.38 ± 6.26 %, respectively. Moreover, the designed hybrid BCI could maintain the same degree of neural engagement as observed when subjects solely performed MI tasks. These phenomena demonstrated the interactivity and clinical utility of the developed system for the rehabilitation of hand function in stroke patients.

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.

Stolwijk-Swüste JM, Brink SM, Voet NBM, Rietman JS, Prange-Lasonder GB. Therapy effect on hand function after home use of a wearable assistive soft-robotic glove supporting grip strength

BACKGROUND

Soft-robotic gloves with an assist-as-needed control have the ability to assist daily activities where needed, while stimulating active and highly functional movements within the user's possibilities. Employment of hand activities with glove support might act as training for unsupported hand function.

OBJECTIVE

To evaluate the therapeutic effect of a grip-supporting soft-robotic glove as an assistive device at home during daily activities.

METHODS

This multicentre intervention trial consisted of 3 pre-assessments (averaged if steady state = PRE), one post-assessment (POST), and one follow-up assessment (FU). Participants with chronic hand function limitations were included. Participants used the Carbonhand glove during six weeks in their home environment on their most affected hand. They were free to choose which activities to use the glove with and for how long. The primary outcome measure was grip strength, secondary outcome measures were pinch strength, hand function and glove use time.

RESULTS

63 patients with limitations in hand function resulting from various disorders were included. Significant improvements (difference PRE-POST) were found for grip strength (+1.9 kg, CI 0.8 to 3.1; p = 0.002) and hand function, as measured by Jebson-Taylor Hand Function Test (-7.7 s, CI -13.4 to -1.9; p = 0.002) and Action Research Arm Test (+1.0 point, IQR 2.0; p≤0.001). Improvements persisted at FU. Pinch strength improved slightly in all fingers over six-week glove use, however these differences didn't achieve significance. Participants used the soft-robotic glove for a total average of 33.0 hours (SD 35.3), equivalent to 330 min/week (SD 354) or 47 min/day (SD 51). No serious adverse events occurred.

CONCLUSION

The present findings showed that six weeks use of a grip-supporting soft-robotic glove as an assistive device at home resulted in a therapeutic effect on unsupported grip strength and hand function. The glove use time also showed that this wearable, lightweight glove was able to assist participants with the performance of daily tasks for prolonged periods.

Patient-Involved Validation of A Somatosensory ERP-BCI Facilitated by Electric Stimulation for Stroke Rehabilitation

Brain-computer interface (BCI) is emerging as an effective complementary solution in the field of rehabilitation for the interaction between patients and robotic assistive devices. Specifically, the somatosensory event-related potentials (ERP) BCI has unique advantage for post-stroke motor rehabilitation scenarios and has been proven feasible on healthy subjects. We conducted the first patient-involved somatosensory ERP-BCI experiment with electric stimulation to evaluate its feasibility for real-world clinical usage. In the experiment, participant selectively attended to electric stimuli applied on either left or right wrist, which represented the operation of robot-assisted exercise of corresponding hand. An integrated platform that included exercise, stimulation, and electroencephalography (EEG) sampling modules was used. For evaluation, we used convolutional neural network (CNN) with transformer module to construct subject-specific intent decoder. The network demonstrated on average 58.95% accuracy in classifying target response from a single ERP trial. When using the classification from multiple consecutive trials, the decoder achieved a maximum of 80.12% mean accuracy in recognizing participants intent, and the highest rate from a single participant was 97.21%. The best information transfer rate (ITR) achieved was 1.956 Bit/min. These results demonstrated that the proposed BCI paradigm could be a valid choice for stroke rehabilitation. In the next stage, we anticipate the involvement of larger patient population, real-time feedback training, and the subsequent quantified motor function recovery results.

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review

This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.

Effectiveness of Syrebo's Glove Rehabilitation Program in a Patient With Middle Cerebral Artery Infarct: A Case Report

In India, stroke is a significant health concern, with an estimated prevalence of around 1.54% in adults over 20 years old. The incidence of stroke in India varies regionally but is generally high due to factors like hypertension and lifestyle changes. Ischemic strokes comprise the majority, particularly in the middle cerebral artery (MCA) territory. MCA stroke presents with diverse symptoms such as weakness, speech difficulties, and vision problems, emphasizing the need for comprehensive rehabilitation. Physiotherapy plays a vital role in addressing these challenges, focusing on strength, coordination, mobility, and independence through tailored interventions. Additionally, soft robotic gloves, such as Syrebo's rehabilitation, offer promising advancements in neurorehabilitation by enhancing motor recovery and functional abilities, particularly in improving grip strength and hand functionality, thus improving outcomes for stroke patients. This case describes a 66-year-old female presenting with sudden left-sided weakness, slurred speech, and facial deviation indicative of bilateral MCA territory infarct. After admission requiring ventilation and medication, imaging confirmed the diagnosis. Following stabilization, she underwent neurophysiotherapy for rehabilitation. Neurological examination revealed deficits in muscle tone, reflexes, cranial nerve function, language, and swallowing. Outcome measures indicated progress in rehabilitation. The case underscores the significance of timely diagnosis and personalized rehabilitation in optimizing outcomes for MCA territory stroke patients.

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.

Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from 40.88±4.54 bits/min to 122.61±7.05 bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.

SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.

Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis

The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0.4193 ± 0.0780 (7 classes) and 0.4032 ± 0.0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0.3590 ± 0.0645 and 0.3159 ± 0.0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.

A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients

Stroke is a common worldwide health problem and a crucial contributor to gained disability. The abilities of people, who are subjected to stroke, to live independently are significantly affected since affected upper limbs' functions are essential for our daily life. This review article focuses on emerging trends in BCI-controlled rehabilitation techniques based on EMG, EEG, or EGM + EEG signals in the last few years. Working on developing rehabilitation robotics, is considered a wealthy scientific area for researchers in the last period. There is a significant advantage that the human acquires from the interaction between the machine and his body, rehabilitation for a patient's limb is very important to get the body limb recovery, and this is what is provided mostly by applying robotic devices.

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.

EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network

Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the feasibility of hand kinematics decoding using EEG cortical source signals has been explored for grasp and lift task. In particular, pre-movement EEG segment is utilized. A residual convolutional neural network (CNN) - long short-term memory (LSTM) based kinematics decoding model is proposed that utilizes motor neural information present in pre-movement brain activity. Various EEG windows at 50 ms prior to movement onset, are utilized for hand kinematics decoding. Correlation value (CV) between actual and predicted hand kinematics is utilized as performance metric for source and sensor domain. The performance of the proposed deep learning model is compared in sensor and source domain. The results demonstrate the viability of hand kinematics decoding using pre-movement EEG cortical source data.

Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis

Background

Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients.

Methods

Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks.

Results

The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011).

Conclusion

Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies.

Clinical Trial Registration

The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.

The Application of Soft Robotic Gloves in Stroke Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Wearable robotic devices have been strongly put into use in both the clinical and research fields of stroke rehabilitation over the past decades. This study aimed to explore the effectiveness of soft robotic gloves (SRGs) towards improving the motor recovery and functional abilities in patients with post-stroke hemiparesis. Five major bibliographic databases, PubMed, Embase, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database, were all reviewed for enrollment regarding comparative trials prior to 7 March 2023. We included adults with stroke and compared their rehabilitation using SRGs to conventional rehabilitation (CR) on hand function in terms of the Fugl-Meyer Upper Extremity Motor Assessment (FMA-UE), Fugl-Meyer Distal Upper Extremity Motor Assessment (FMA-distal UE), box and blocks test score, grip strength test, and the Jebsen-Taylor hand function test (JTT). A total of 8 studies, comprising 309 participants, were included in the analysis. Compared to CR, rehabilitation involving SRGs achieved better FMA-UE (MD 6.52, 95% CI: 3.65~9.39), FMA-distal UE (MD 3.27, 95% CI: 1.50~5.04), and JJT (MD 13.34, CI: 5.16~21.53) results. Subgroup analysis showed that stroke latency of more than 6 months and training for more than 30 min offered a better effect as well. In conclusion, for patients with stroke, rehabilitation using SRGs is recommended to promote the functional abilities of the upper extremities.

CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation

Brain-computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.