Towards an enhanced ERP speller based on the visual processing of face familiarity

In this study, a novel P300 based brain-computer interface (BCI) system using random set presentation pattern and employing the effect of face familiarity has been proposed and developed. While the effect of face familiarity is widely studied in the cognitive neurosciences, it has so far not been addressed for the purpose of BCI. We compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our novel approach. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli and thereby improving P300-based spelling performance. This leads to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.