A Hybrid Brain-Computer Interface Fusing P300 ERP and Electrooculography

Abstract

An Electrooculography-based method is used to correct misclassification of P300 event related potentials in a Lateral Character Speller (LSC) Brain-Computer Interface (BCI). The LSC speller’s circular layout allows us to combine P300 detection with the detection of eye movements to improve symbol detection reliability. We separately classify the vertical and horizontal components of Electrooculography signals from shifts in user gaze during intertrial intervals, determining the quadrant of the character the participant will focus on in the next trial. A P300 EEG-based classification decision can then be corrected using quadrant information, selecting the character with the highest probability on that quadrant. This paper focuses on the implementation of the EOG quadrant detector. Preliminary results show good lateral identification but a lower selection accuracy. Empirically, it was possible to conclude that a relatively high percentage of P300 classification errors were corrected using lateral information alone, significantly increasing LSC character selection accuracy.