Identifying Neurophysiological Markers of Movement Quality Progress in the Context of BCI Post-Stroke Rehabilitation
Stroke is a debilitating medical condition, often leading to severe impairments of motor function. Recently, brain computer interface (BCI) systems have been proposed as a novel and promising tool in post-stroke rehabilitation. BCI rehabilitation systems aim to promote activity-dependent plasticity by providing real-time sensory feedback based on user’s neural activity . Immersive virtual reality (VR) has become an increasingly popular way of presenting feedback, as it has been shown to promote engagement in users and enhance a sense of embodiment . While VR-BCI systems have been found to have a positive impact on functional motor recovery , research rarely reports on neurophysiological changes associated with the motor learning process. This study aims to address this gap by examining electroencephalographic (EEG) activity of subjects as they perform repeated sessions of a VR-based motor rehabilitation game.
The study will include five healthy subjects. Each will complete five sessions of a game, developed for future use in VR-based motor rehabilitation (with adapted difficulty levels for healthy players). The game requires a subject to perform several upper limb movement tasks (e.g., pouring water into a cup) in a repeated manner, while the HMD’s hand-tracking feature detects performed
movements. Behavioural data, specifically, the number of correct trials and reaction times, will be combined with EEG data collected during the session on a 128- electrode system. Changes in EEG activity will be examined in reference to subjects’ improvement in task performance with the aim to identify possible neurophysiological markers of movement quality progress.
Expected Results & Discussion
We expect the improvement in movement quality to be associated with stronger desynchronization at mu frequency band (8–13 Hz) , as well as changes in motor cortex connectivity . Identifying consistent EEG markers of movement quality progress would potentially contribute towards improved neurofeedback in rehabilitation BCI systems, paving the way for accessible in- home rehabilitation for stroke survivors.
 M. A. Cervera et al., “Brain computer interfaces for post-stroke motor rehabilitation: a meta-analysis,” Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651–663, 2018.
 A. Vourvopoulos et al., “Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report,” Frontiers in Human Neuroscience, vol. 13, pp. 244, 2019.
 J. Wu et al., “Resting-state cortical connectivity predicts motor skill acquisition,” NeuroImage, vol. 91, pp. 84– 90, 2014.