Abstract & Overview
Abstract Recently, much attention has been paid to Hybrid Brain-Computer Interfaces (BCI). In this study, we developed a hybrid BCI speller that simultaneously utilized information from both hand Electromyography (EMG) and SSVEP. This cross-modal BCI speller could increase the target number so as to enhance the information transfer rate (ITR). A 60-target hybrid BCI speller was built in this study. A frame-based sampled sinusoidal stimulation method was used to generate the flickering stimulus on the LCD screen. The 60 targets were equally divided into 4 sections, and each section had the same frequency range. EMG signal was used to distinguish different sections. Subjects were required to repeatedly make a fist from 0 to 3 times when the target was shown in section 1 to section 4. Then by extracting the envelope of the EMG signal and calculating the number of peaks of the envelope, we could know which section the target was in. Canonical Correlation Analysis (CCA) method was used to classify the SSVEP signal. The offline results showed that ITR achieved maximum value when the time window was set to be 2 s. The average classification accuracy of a 2 s time window was 80.5% and information transfer rate was 83.2 bit/min using the proposed hybrid BCI system. While the ITR was 32.7 bit/min for EMG only condition and 58.2 bit/min for SSVEP only condition, which revealed that the hybrid system had better performance than the two single-modal modalities.