  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.
