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Objective: Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) is a promising technology for neurorehabilitation after stroke. However, many face challenges in using a BCI because they fail to produce discriminable patterns in their brain activity. Personalizing the BCI task difficulty could help the learning process of these users but there is currently very limited knowledge on which methods can be used online. Our aim was to explore a distance-to-bound approach for adapting MI BCI task difficulty in real time.

Approach: Two chronic stroke patients performed 12 BCI training sessions over 4 weeks during which they performed MI of open- and close hand movements and received continual visual feedback based on multivariate decoding of ongoing electroencephalogram (EEG) activity. The difficulty was increased and adapted in real time based on distance-to-bound decoding metrics and using a multiple-session design we investigated the stability of this approach and how it related to MI-related EEG activity of each patient.

Main results: We show that patients had to produce stronger alpha and beta event-related desynchronization (ERD) activity across the sensorimotor cortical areas of the brain to receive positive feedback. In addition, we show that the online adaptation converged within sessions as well as accommodating for drift in the data both within and between sessions. We suggest that the distance-to-bound approach can effectively be used to control BCI task difficulty and potentially guide patients to produce functionally relevant activity patterns. However, from our results, stronger sensorimotor ERD activity did not consistently correlate to improved motor function. Clinical assessments showed that both patients improved in motor function (+4 and +8.7 change in Fugl-Meyer assessment for upper extremity), however, the correlation to sensorimotor ERD activity was positive for one patient and negative for the other (Pearson’s rho = 0.95, -0.80, p = 0.05, 0.18). . Further, MI pattern strength correlated with clinical motor outcomes (Pearson’s rho = 0.993, -0.849, p = 0.007, 0.151), however positively for one patient and negatively for the other.These results indicate that the translation of distance-to-bound outputs to feedback needs to be individually tailored considering the stroke lesion and EEG activity profiles for each patient.

Significance: This study provides valuable insights and considerations for BCI difficulty adaptation in the aim of developing more effective training protocols in BCI-based stroke rehabilitation.

Objective: Neurofeedback training using motor imagery-based brain-computer interfaces (MI-BCIs) show promising but variable outcomes in chronic stroke rehabilitation. However, little is known on the dual task effects of feedback when simultaneously performing a mental task. This becomes especially relevant for cognitively challenged patient groups. This study aims to investigate instantaneous effects of continual visual feedback on neurophysiological activity while stroke patients practiced MI during BCI training.

Approach: Fourteen chronic stroke patients participated in a 10-week long intervention, with 4 weeks of intense MI-BCI training. During each MI training trial, patients were instructed to mentally imagine either opening or closing their paretic hand while being provided continually updated visual neurofeedback only during the last part of the trial (after 4 seconds). We analysed patient-specific spatial and temporal dynamics of peak frequency power related to MI with and without feedback, and analysed the correlation of changes to blink rate, and clinical motor- and cognitive abilities.   

Main results: Individual MI-related peak frequency of event-related desynchronisation (ERD) across sensorimotor areas was mainly found within the Alpha band. Two peak ERD patient profiles were observed: dominant ipsilesional (43\% of patients) or contralesional ERD (57\% of patients). Independent of ERD profile, we show weakened ERD in anticipation of visual feedback, followed by a restrengthening shortly after feedback onset, not significantly different from the prior no feedback phase. However, bilateral occipital decrease in individual peak frequency suggests increased information processing shortly after feedback onset. Furthermore, ERD was weakened during the last part of the trial (with feedback). This reduction was correlated to increased blink rate, and we show a tendency of higher cognitive ability for patients with greater reduction in ERD during the end of the trial. These results indicate that visual feedback does not interfere, nor significantly help the practice of MI during BCI training. 

Significance: This study provides insight in the temporal and spatial dynamics of peak frequency ERD in response to visual feedback while stroke patients practice MI during BCI training.  We believe the results will contribute to building a solid foundation of knowledge and understanding for the informed development of BCI technology in stroke rehabilitation.