Exploring new territory: Calibration-free decoding for c-VEP BCI
This research addresses the usability and accessibility of BCIs by reducing setup time, though it appears incremental as it adapts an existing method to a new protocol.
This study tackled the problem of eliminating calibration sessions in brain-computer interfaces (BCIs) by exploring two zero-training methods, unsupervised mean maximization (UMM) and canonical correlation analysis (CCA), for code-modulated visual evoked potential (c-VEP) stimulus protocols, showing their effectiveness in navigating c-VEP dataset complexities.
This study explores two zero-training methods aimed at enhancing the usability of brain-computer interfaces (BCIs) by eliminating the need for a calibration session. We introduce a novel method rooted in the event-related potential (ERP) domain, unsupervised mean maximization (UMM), to the fast code-modulated visual evoked potential (c-VEP) stimulus protocol. We compare UMM to the state-of-the-art c-VEP zero-training method that uses canonical correlation analysis (CCA). The comparison includes instantaneous classification and classification with cumulative learning from previously classified trials for both CCA and UMM. Our study shows the effectiveness of both methods in navigating the complexities of a c-VEP dataset, highlighting their differences and distinct strengths. This research not only provides insights into the practical implementation of calibration-free BCI methods but also paves the way for further exploration and refinement. Ultimately, the fusion of CCA and UMM holds promise for enhancing the accessibility and usability of BCI systems across various application domains and a multitude of stimulus protocols.