Phase synchrony analysis for SSVEP-based BCIs

Brain-computer interfaces (BCI) based on Steady State Visual Evoked Potential (SSVEP) can provide higher throughput than other BCI modalities. For the sake of safety and comfort, the frequencies of the stimulus should be higher than 30 Hz. However, only a limited number of these frequencies can elicit SSVEPs that are strong enough for BCI purposes. In order to increase the number of available stimuli, the SSVEP phase can be taken into account. In this study, we used phase synchrony analysis to extract the phase difference between SSVEP and stimuli as a feature to identify a subject's intention. This analysis can mitigate the adverse effect brought by the phase deviation that may occur in the stimuli. Furthermore, the classification accuracy when using a single lead signal (Oz-Cz) is compared to a spatial filtered signal. The result shows that the phase synchrony analysis can effectively extract the phase difference and that spatial filtering can significantly increase the classification accuracy.