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 electroencephalo- graphic (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 app- roach to BCI systems is illustrated on an eye closure dependent BCI and

on a motor imagery based BCI. Results show that the proposed autore- gressive switching multiple model approach offers a unified framework of

detecting multiple modes, even in the presence of limited training data.