A new Approach for Hybrid BCI speller based on P300 and SSVEP

P300 and steady state visual evoked potential (SSVEP) are type of electroencephalography (EEG) phenomena that widely used in braincomputer interface (BCI) systems since both of them have high signal response and signal noise ratio. Classification accuracy rate ofsignal, and signal detection time affect overall performance of BCI systems. These both values are used for calculation informationtransfer rate (ITR) that is a key performance indicator for a BCI system. A P300 based BCI or a SSVEP based BCI have higher ITRvalues than other type of BCI systems. Thus, in this study our aim was to use together these both P300 and SSVEP phenomena in a BCIspeller. We proposed a hybrid BCI speller based on P300 and SSVEP. Moreover, our proposed BCI speller interface allows to use onlyP300 stimuli, only SSVEP stimuli, or hybrid stimuli. In this BCI speller, there are numbers in 3 × 3 matrix form for elicitind P300 signaland also 9 white square flickering objects were placed near numbers for eliciting SSVEP. In this research, experiments were performedin two stage (training and online stages) with three sessions (only SSVEP stimuli session, only P300 stimuli session, and hybrid session).Five subjects participated experiments. We used support vector machine method for detection of P300 signal and SSVEP. According toexperiment results, average classification accuracy values were 83.78%, 84.67%, and 90.89% with using only SSVEP stimuli, onlyP300 stimuli, and hybrid stimuli, respectively. Furhermore, average information transfer rate values were 6.81, 6.97, and, 8.19 bit/minwith using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Results showed that the proposed hybrid BCI spellerbased on P300 and SSVEP reached higher classification accuracy and ITR values than using only SSVEP stimuli or only P300 stimulibased BCI spellers.