Optimizing visual comfort and classification accuracy for a hybrid P300-SSVEP brain-computer interface

Abstract

Visual brain-computer interfaces (BCIs) have achieved great progress in speed recently. But the problem of visual fatigue caused by intense flashes poses a great challenge in designing practical systems for long-term use. A direct way to improve visual comfort is to reduce the stimulus contrast. But it could also weaken the featured evoked potentials, which would bring a negative impact on system accuracy. Thus it's significant to figure out the optimal contrast that could have both high visual comfort and high accuracy. This study investigated the effects of different stimulus contrasts on the two aspects. Six hybrid P300-SSVEP spellers were developed with different stimulus contrasts. Three subjects spelled 10 same characters offline for each speller. After each spelling subjects were asked to grade the flashes they just met in terms of visual comfort. Stepwise linear discriminant analysis (SWLDA) was used to recognize the P300 potential; the filter bank canonical correlation analysis (FBCCA) with individual template was adopted to classify the SSVEP. A decision fusion was performed to recognize the target. The results showed that, compared with P300 or SSVEP only features, the hybrid features significantly improved the accuracy. The subjects felt more comfortable for contrasts below 25%. The classification accuracy wouldn't have a great loss unless the contrast was below 12%. Taken together, the optimal contrast was 12% for the hybrid P300-SSVEP BCI system in this study.