Prediction of Motor-Imagery-BCI performance using Median Nerve Stimulation

Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) have grown significantly in recent years and have successfully overcome several challenges in rehabilitation, control, communication, artistic creation, etc. However, not everyone can use MI-BCI, and there are large variations between users in performance. Thus, predicting a user's future MI-BCI performance is now a critical issue that could enable the BCI system to be adapted as effectively as possible to each user, thereby improving its efficiency. However, few methods exist that can currently predict performance prior to a BCI session. In fact, both neurophysiological markers and questionnaires have been shown to be insufficient as predictive tools. Interestingly, it has previously been shown that painless and passive median nerve stimulation can generate well-known motor patterns of Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS). In this paper, we propose to use these post-simulation motor patterns to predict the future performance of an MI-BCI user. Our results show that the ERD after stimulation of the median nerve in the beta frequency band is correlated with the BCI performance of the subjects (rho = −0.58; p-value < 0.05) and that the ERD in the mu band correlates very strongly with BCI performance in this specific frequency band (rho = −0.87; p-value < 0.001). These promising results suggest that there is a strong neural predisposition to perform well with MI-BCI and that median nerve stimulation may be an easy-to-install, fast and highly effective technique for detecting it.