Background: Brain-computer interface (BCI) has been regarded as a newly developing intervention in promoting motor recovery in stroke survivors. Several studies have been performed in chronic stroke to explore its clinical and subclinical efficacy. However, evidence in subacute stroke was poor, and the longitudinal sensorimotor rhythm changes in subacute stroke after BCI with exoskeleton feedback were still unclear. Materials and Methods: Fourteen stroke patients in subacute stage were recruited and randomly allocated to BCI group (n = 7) and the control group (n = 7). Braincomputer interface training with exoskeleton feedback was applied in the BCI group three times a week for 4 weeks. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) scale was used to assess motor function improvement. Brain-computer interface performance was calculated across the 12-time interventions. Sensorimotor rhythm changes were explored by event-related desynchronization (ERD) changes and topographies. Results: After 1 month BCI intervention, both the BCI group (p = 0.032) and the control group (p = 0.048) improved in FMA-UE scores. The BCI group (12.77%) showed larger percentage of improvement than the control group (7.14%), and more patients obtained good motor recovery in the BCI group (57.1%) than did the control group (28.6%). Patients with good recovery showed relatively higher online BCI performance, which were greater than 70%. And they showed a continuous improvement in offline BCI performance and obtained a highest value in the last six sessions of interventions during BCI training. However, patients with poor recovery reached a platform in the first six sessions of interventions and did not improve any more or even showed a decrease. In sensorimotor rhythm, patients with good recovery showed an enhanced ERD along with time change. Topographies showed that the ipsilesional hemisphere presented stronger activations after BCI intervention.

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10–50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = −0.732, p < 0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r = 0.641, p < 0.001). Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.

Background. Camera technique-based mirror visual feedback (MVF) is an optimal interface for mirror therapy. However, its efficiency for stroke rehabilitation and the underlying neural mechanisms remain unclear. Objective. To investigate the possible treatment benefits of camera-based MVF (camMVF) for priming prior to hand function exercise in subacute stroke patients, and to reveal topological reorganization of brain network in response to the intervention. Methods. Twenty subacute stroke patients were assigned randomly to the camMVF group (MG, N = 10) or a conventional group (CG, N = 10). Before, and after 2 and 4 weeks of intervention, the Fugl-Meyer Assessment Upper Limb subscale (FMA_UL), the Functional Independence Measure (FIM), the modified Ashworth Scale (MAS), manual muscle testing (MMT), and the Berg Balance Scale (BBS) were measured. Resting-state electroencephalography (EEG) signals were recorded before and after 4-week intervention. Results. The MG showed more improvements in the FMA_UL, the FMA_WH (wrist and hand), and the FIM than the CG. The clustering coefficient (CC) of the resting EEG network in the alpha band was increased globally in the MG after intervention but not in the CG. Nodal CC analyses revealed that the CC in the MG tended to increase in the ipsilesional occipital and temporal areas, and the bilateral central and parietal areas, suggesting improved local efficiency of communication in the visual, somatosensory, and motor areas. The changes of nodal CC at TP8 and PO8 were significantly positively correlated with the motor recovery. Conclusions. The camMVF-based priming could improve the motor recovery, daily function, and brain network segregation in subacute stroke patients.