论文部分内容阅读
Objective: Spelling is one of the most important issues in brain-computer interface(BCI)research.This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode.Approach:The parallel-BCI speller was constitutive of four independent P300+SSVEP-B(P300 plus SSVEP blocking)spellers with different flicker frequencies,thereby all characters had a specific time-frequency code.To verify its effectiveness,eleven subjects were involved in the offline and online spellings.A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis(CCA)and stepwise linear discriminant analysis(SWLDA).Results:Online spellings showed that the proposed parallel-BCI speller had a high performance,reaching the highest information transfer rate of 67.4 bit/min,with an average of 54.0 bit/min and 43.0 bit/min in the 3 rounds and 5 rounds,respectively.Significance:The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers,and highly improved the BCI spelling performance.