On the Use of Match Filtering for the P300 Detection

The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant stimuli, located in the parietal lobe of the brain. P300s are increasingly used in brain-computer interfaces (BCI) because the users of ERP-based BCIs need no special training. In order to detect P300s, most studies in the field have been focused on a supervised approach, dealing with over-fitting filters and the need for later validation. In this paper we start bridging this gap by modeling an unsupervised classifier of the P300 presence based on a weighted score. This is carried out through the use of matched filters that weight more heavily events that are likely to represent the P300 wave. The optimal weights are determined through a study of the data's features. The combination of different artifact cancellation methods and P300 extraction techniques provides a marked, statistically significant, improvement in accuracy at the level of the top-performing algorithms for a supervised approach presented in the literature to date. This innovation brings a notable impact in ERP-based communicators, appointing to the development of a faster and more reliable BCI technology.