Brain-computer interface treatment for gait rehabilitation in stroke patients

Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG

Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.

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).

Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients

BACKGROUND

The most challenging aspect of rehabilitation is the repurposing of residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical rehabilitation programs have been developed. This study aimed to investigate the effects of motor imagery (MI)-based brain-computer interface (BCI) rehabilitation programs on upper extremity hand function in patients with chronic hemiplegia.

DESIGN

A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial.

METHODS

Forty-six eligible stroke patients with upper limb motor dysfunction participated in the study, six of whom dropped out. The patients were randomly divided into a BCI group and a control group. The BCI group received BCI therapy and conventional rehabilitation therapy, while the control group received conventional rehabilitation only. The Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) score was used as the primary outcome to evaluate upper extremity motor function. Additionally, functional magnetic resonance imaging (fMRI) scans were performed on all patients before and after treatment, in both the resting and task states. We measured the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion of ALFF (zALFF), and z conversion of ReHo (ReHo) in the resting state. The task state was divided into four tasks: left-hand grasping, right-hand grasping, imagining left-hand grasping, and imagining right-hand grasping. Finally, meaningful differences were assessed using correlation analysis of the clinical assessments and functional measures.

RESULTS

A total of 40 patients completed the study, 20 in the BCI group and 20 in the control group. Task-related blood-oxygen-level-dependent (BOLD) analysis showed that when performing the motor grasping task with the affected hand, the BCI group exhibited significant activation in the ipsilateral middle cingulate gyrus, precuneus, inferior parietal gyrus, postcentral gyrus, middle frontal gyrus, superior temporal gyrus, and contralateral middle cingulate gyrus. When imagining a grasping task with the affected hand, the BCI group exhibited greater activation in the ipsilateral superior frontal gyrus (medial) and middle frontal gyrus after treatment. However, the activation of the contralateral superior frontal gyrus decreased in the BCI group relative to the control group. Resting-state fMRI revealed increased zALFF in multiple cerebral regions, including the contralateral precentral gyrus and calcarine and the ipsilateral middle occipital gyrus and cuneus, and decreased zALFF in the ipsilateral superior temporal gyrus in the BCI group relative to the control group. Increased zReHo in the ipsilateral cuneus and contralateral calcarine and decreased zReHo in the contralateral middle temporal gyrus, temporal pole, and superior temporal gyrus were observed post-intervention. According to the subsequent correlation analysis, the increase in the FMA-UE score showed a positive correlation with the mean zALFF of the contralateral precentral gyrus (r = 0.425, P < 0.05), the mean zReHo of the right cuneus (r = 0.399, P < 0.05).

CONCLUSION

In conclusion, BCI therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. The correlation of the zALFF of the contralateral precentral gyrus and the zReHo of the ipsilateral cuneus with motor improvements suggested that these values can be used as prognostic measures for BCI-based stroke rehabilitation. We found that motor function was related to visual and spatial processing, suggesting potential avenues for refining treatment strategies for stroke patients.

TRIAL REGISTRATION

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

Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system

Introduction

Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions.

Methods

Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively.

Results

Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed.

Discussion

The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.