Segmentation and labelling of eeg for brain computer interfaces

Abstract

Segmentation and labelling of time series is a common requirement for several applications. A brain computer interface (BCI) is achieved by classification of time intervals of the electroencephalographic (EEG) signal and thus requires EEG signal segmentation and labelling. This work investigates the use of an autoregressive model, extended to a switching multiple modelling framework, to automatically segment and label EEG data into distinct modes of operation that may switch abruptly and arbitrarily in time. The applicability of this approach to BCI systems is illustrated on an eye closure dependent BCI and on a motor imagery based BCI. Results show that the proposed autoregressive switching multiple model approach offers a unified framework of detecting multiple modes, even in the presence of limited training data.