The parallel-BCI speller based on the P300 and SSVEP features

This paper developed a parallel-BCI speller system, consisting of four simultaneously presented sub-spellers, where the sub-speller switching and character selection were simultaneously identified through decoding the dominant frequency of steady-state visual-evoked potential (SSVEP) and the time of P300 occurrence in a parallel mode. Five subjects took part in the offline and online experiments. The canonical correlation analysis (CCA) and stepwise linear discriminant analysis (SWLDA) were jointly used to recognize the target character. Online tests showed that the parallel-BCI speller reached the peak information transfer rate (ITR) of 67.4 bit/min with an average of 48.0 bit/min. The results indicate that the proposed parallel-BCI system can be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improve the BCI spelling performance.