Hybrid BCI utilising SSVEP and P300 event markers for reliable and improved classification using LED stimuli

Abstract

This paper investigates the possibilities of developing a hybrid brain-computer interface based on Steady State Visual Evoked Potential (SSVEP) and P300 responses. SSVEP classification accuracy is improved using P300 event detection as a secondary validation technique in this study. SSVEP events are generated using a hybrid visual stimuli consisting of four independent radial chip-on-board green LED rings flashing at frequencies 7, 8 9 and 10 Hz, which are controlled by four 32-bit microcontrollers to ensure precise generation of flashing frequencies. P300 events are generated with a flash stimulus controller that produces random red LED flashes using high power single LED located inside each of the four radial rings. The P300 flashes are marked as events along with the recorded SSVEP EEG. The study analysed the EEG data recorded from five participants comprising of five trials each, which included both SSVEP and P300 events to identify the classification effectiveness for hybrid BCI. The EEG data was band-pass filtered and events extracted using custom MATLAB algorithms showed that SSVEP classifications could be improved using P300 events for reliable BCI applications.