Reliable predictor of BCI motor imagery performance using median nerve stimulation.

Abstract

Objective.Predicting performance in brain-computer interfaces (BCIs) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual.Approach.This study explores the use of median nerve stimulation (MNS) as a predictor of motor imagery (MI)-BCI performance. MNS induces event related (de)synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction.Main results.Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71,p<0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0,p<0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance.Significance.By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery.