Detection of attention shift for asynchronous P300-based BCI

Abstract

Brain-computer interface (BCI) provides patients suffering from severe neuromuscular disorders an alternative way of interacting with the outside world. The P300-based BCI is among the most popular paradigms in the field and most current versions operate in synchronous mode and assume participant engagement throughout operation. In this study, we demonstrate a new approach for assessment of user engagement through a hybrid classification of ERP and band power features of EEG signals that could allow building asynchronous BCIs. EEG signals from nine electrode locations were recorded from nine participants during controlled engagement conditions when subjects were either engaged with the P3speller task or not attending. Statistical analysis of band power showed that there were significant contrasts of attending only for the delta and beta bands as indicators of features for user attendance classification. A hybrid classifier using ERP scores and band power features yielded the best overall performance of 0.98 in terms of the area under the ROC curve (AUC). Results indicate that band powers can provide additional discriminant information to the ERP for user attention detection and this combined approach can be used to assess user engagement for each stimulus sequence during BCI use.