Automatic classification of multivariate EEGs using an amount of information measure and the eigenvalues of parametric time series model features

Abstract

Two new classes of features are introduced for the automatic classification of multichannel stationary time series EEG data. The features are the Shannon-Gelfand-Yaglom measure of the amount of information between two sets of stationary Gaussian time series and the eigenvalues computed from a parametric model of the time series. The performance of these features for automatic sleep stage scoring from two EEG data channels, evaluated using the multinomial logistic function, is presented as an example. This parametric modeled EEG time series-two features for classification approach is a radical departure from the more conventional windowed periodogram spectral analysis-discriminant analysis packaged computer program approach.