Enhancing Motor-Imagery Brain-Computer Interface Training With Embodied Virtual Reality: A Pilot Study With Older Adults

Electroencephalography-based Brain-Computer Interfaces (BCI’s) can provide an alternative non-muscular channel of control to stroke survivors, especially to those who lack volitional movement. This is achieved through motor-imagery (MI) practice, involving the activation of motor-related brain regions. MI is reinforced in a closed-loop BCI through rewarding feedback, and it has been shown to be able to strengthen key motor pathways. Recently, growing evidence of the positive impact of virtual reality (VR) has accumulated. When combined with BCI, VR can provide patients with a safe simulated environment for rehabilitation training, which could be adapted to real-world scenarios. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI, a problem known as BCI illiteracy. In this study, we investigate the role of embodied feedback and how we can help elderly adults increase their BCI performance during MI-BCI training in VR. The elderly population was selected to age-match with the typical stroke age-range demographic, accounting for age-related confounds. Participants have received MI-BCI training in two conditions: Abstract feedback (Graz BCI), and embodied feedback (NeuRow VR-BCI). Current results show differences between the two conditions in terms of Event-Related Desynchronization (ERD), lateralization of ERD and classifier performance in terms of arm discriminability.