EEG-based brain-computer interface to a virtual walking avatar engages cortical adaptation

Recent advances in brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users' gait intent and continuous kinematics. However, the cortical adaptation and the dynamics of cortical involvement in human upright walking with a closed-loop BCI in virtual environment (VE) have yet to be demonstrated. To address explore this possibility, we designed a closed-loop BCI to allow users to control a virtual avatar to walk using their encephalography (EEG). Delta band EEG (0.1-3 Hz) was used as the main feature in prediction. Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar. The average decoding accuracies (Pearson's r values) across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8. Source localization revealed significant differences in cortical network activity between walking with and without closed-loop BCI control of the virtual avatar. This current study demonstrates the feasibility of using a closed-loop EEG-based BCI-VR to trigger cortical adaptation, promoting cortical involvement, and monitoring cortical activity from non-invasive EEG. Our system may be relevant for neurological gait rehabilitation as a clinical tool for post-stroke physical training and clinical assessment.