Neurophysiological screening of individual variability for robust decoding in c-VEP-based BCI

Abstract Code-modulated visual evoked-potential (c-VEP)-based reactive brain–computer interfaces (BCIs) deliver high information-transfer rates with minimal calibration, yet performance often collapses when models are transferred between users. We, therefore, pursue a two-fold aim: first, to pinpoint neurophysiological predictors that explain this inter-participant variability; second, to identify a decoding pipeline that sustains accuracy across users in a burst-c-VEP paradigm (brief, aperiodic flashes at 3 Hz). From 24 participants, we find that stronger inter-epoch correlation (R≈0.80), larger peak-to-peak amplitude of the flash-VEP, larger α bandpower, larger θ bandpower, and lower δ bandpower are five neurophysiological predictors that correlate between high performers (>90% accuracy) and low performers (