P300 brainwave extraction from EEG signals: An unsupervised approach

Abstract

The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant stimuli, arisen from the frontal, temporal and occipital lobe of the brain, although is usually measured in the parietal lobe. P300 signals are increasingly used in brain-computer interfaces (BCI) because the users of ERP-based BCIs need no special training. In order to detect the P300 signal, 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 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 cancelation methods and the 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.