Optimizing Phase Intervals for Phase-Coded SSVEP-Based BCIs With Template-Based Algorithm

Recent studies have shown that integrating individualized templates into a template-matching target identification method could significantly improve the performance of a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However, collecting the template (or calibration) data for each individual can be time-consuming and laborious. This issue can be alleviated by employing phase-coded visual stimuli because phase information could be discriminated by using templates synthesized from the template induced by a visual stimulus. Minimizing phase intervals between two adjacent visual stimuli could increase the number of stimuli without increasing the calibration cost. Nonetheless, no study has investigated the effects of the phase interval on the classification performance. This study compared the classification accuracy of SSVEPs with five different phase intervals (0.1 π, 0.2 π, 0.3 π, 0.4 π, and 0.5 π) using synthesized individual templates with task-related component analysis (TRCA)-based spatial filtering. From a public 12-class SSVEP dataset, phase-adjusted SSVEP data were created by adding time shifts according to the five phase intervals. The classification results showed that the accuracy was sufficiently high when the phase intervals were over 0.3 π, suggesting the use of up to six phase-shifted visual stimuli at a given frequency.