Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N= 31) and healthy populations (N= 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

Electromyography (EMG) is an integral part of many biomedical and healthcare applications. It has been used as a metric for tracking rehabilitation progress and identifying diseases that affect muscle activation patterns. Although it is widely used in many disciplines, conventional EMG recording and interpretation techniques lack in providing precise signal detection and robust classification accuracy. In recent years, thanks to advances in both material science and artificial intelligence, EMG detection techniques are improving at a rapid pace. Materials that allow for enhanced biocompatibility have improved the quality of data recorded by electrodes. The use of machine learning algorithms has paved new ways to understand complex EMG signals, triggering diverse, novel, and improved application scenarios within the healthcare framework. To help readers establish a clear picture of the two most important components in EMG technology, i.e., electrodes and algorithms, while catching up with the latest research outcomes, this review article is composed. The article starts by introducing conventional EMG electrode materials and architectures, then explains how state‐of‐the‐art works have improved electrode utilization. Subsequently, EMG signal conditioning and interpretation algorithms are investigated. Finally, current challenges in the research domain and authors’ perspectives are discussed.

Since the underlying mechanisms of neurorehabilitation are not fully understood, the prognosis of stroke recovery faces significant difficulties. Recovery outcomes can vary when undergoing different treatments; however, few models have been developed to predict patient outcomes toward multiple treatments. In this study, we aimed to investigate the potential of predicting a treatment's outcome using a deep learning prognosis model developed for another treatment. A total of 15 stroke survivors were recruited in this study, and their clinical and physiological data were measured before and after the treatment (clinical measurement, biomechanical measurement, and electroencephalography (EEG) measurement). Multiple biomarkers and clinical scale scores of patients who had completed manual stretching rehabilitation training were analyzed. Data were used to train deep learning prognosis models, yielding an 87.50% prognosis accuracy. Pre-trained prognosis models were then applied to patients who completed robotic-assisted stretching training, yielding a prognosis accuracy of 91.84%. Interpretation of the deep learning models revealed several key factors influencing patients' recoveries, including the plantar-flexor active range of movement (r = 0.930, P = 0.02), dorsiflexor strength (r = 0.932, P = 0.002), plantar-flexor strength (r = 0.930, P = 0.002), EEG power spectrum density and EEG functional connectivities in the occipital, central parietal, and parietal areas. Our results suggest (i) that deep learning can be a promising method for accurate prediction of the recovery potential of stroke patients in clinical scenarios and (ii) that it can be successfully applied to different rehabilitation trainings with explainable factors.

Background: Ankle function impairment is a critical factor impairing normal walking in survivors of stroke. The soft robotic exoskeleton (SRE) is a novel, portable, lightweight assistive device with promising therapeutic potential for gait recovery during post-stroke rehabilitation. However, whether long-term SRE-assisted walking training influences walking function and gait quality in patients following subacute stroke is unknown. Therefore, the primary objective of this study was to assess the therapeutic effects of SRE-assisted walking training on clinical and biomechanical gait outcomes in the rehabilitation of patients with subacute stroke.Methods: A group patients who had experienced subacute stroke received conventional rehabilitation (CR) training combined with 10-session SRE-assisted overground walking training (30 min per session, 5 sessions/week, 2 weeks) (SRE group, n = 15) compared with the control group that received CR training only (CR group, n = 15). Clinical assessments and biomechanical gait quality measures were performed pre-and post-10-session intervention, with the 10-Minute Walk Test (10MWT) and 6-Minute Walk Test (6MWT) used to define the primary clinical outcome measures and the Functional Ambulation Category, Fugl-Meyer Assessment for Lower Extremity (FMA-LE) subscale, and Berg Balance Scale defined the secondary outcome measures. The gait quality outcome measures included spatiotemporal and symmetrical parameters during walking.Results: After the 10-session intervention, the SRE and CR groups exhibited significant within-group improvements in all clinical outcome measures (p < 0.05). Between-comparison using covariance analyses demonstrated that the SRE group showed greater improvement in walking speed during the 10MWT (p < 0.01), distance walked during the 6MWT (p < 0.05), and FMA-LE scores (p < 0.05). Gait analyses showed that the SRE group exhibited significantly improved spatiotemporal symmetry (p < 0.001) after 10-session training, with no significant changes observed in the CR group.Conclusion: Compared with CR training, SRE-assisted walking training led to greater improvements in walking speed, endurance, and motor recovery. Our findings provide preliminary evidence that SRE may be considered for inclusion in intensive gait training clinical rehabilitation programs to further improve walking function in patients who have experienced stroke.

Brain plasticity, including anatomical changes and functional reorganization, is the physiological basis of functional recovery after spinal cord injury (SCI). The correlation between brain anatomical changes and functional reorganization after SCI is unclear. This study aimed to explore whether alterations of cortical structure and network function are concomitant in sensorimotor areas after incomplete SCI. Eighteen patients with incomplete SCI (mean age 40.94 ± 14.10 years old; male:female, 7:11) and 18 healthy subjects (37.33 ± 11.79 years old; male:female, 7:11) were studied by resting state functional magnetic resonance imaging. Gray matter volume (GMV) and functional connectivity were used to evaluate cortical structure and network function, respectively. There was no significant alteration of GMV in sensorimotor areas in patients with incomplete SCI compared with healthy subjects. Intra-hemispheric functional connectivity between left primary somatosensory cortex (BA1) and left primary motor cortex (BA4), and left BA1 and left somatosensory association cortex (BA5) was decreased, as well as inter-hemispheric functional connectivity between left BA1 and right BA4, left BA1 and right BA5, and left BA4 and right BA5 in patients with SCI. Functional connectivity between both BA4 areas was also decreased. The decreased functional connectivity between the left BA1 and the right BA4 positively correlated with American Spinal Injury Association sensory score in SCI patients. The results indicate that alterations of cortical anatomical structure and network functional connectivity in sensorimotor areas were non-concomitant in patients with incomplete SCI, indicating the network functional changes in sensorimotor areas may not be dependent on anatomic structure. The strength of functional connectivity within sensorimotor areas could serve as a potential imaging biomarker for assessment and prediction of sensory function in patients with incomplete SCI. This trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR-ROC-17013566).