Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary...

Objective.This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency.Approach.In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied.Main Results.Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort.Significance.This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.