Parametric time series models for multivariate EEG analysis.

Abstract The autoregressive (AR) and mixed autoregressive-moving average (AR-MA) parametric models of stationary time series are of current interest for the purposes of spectral analysis and for the extraction of features for automatic EEG classification. Procedures for computing AR models and a new two-stage least-squares procedure for computing AR-MA models of multivariate time series are shown. The results of spectral estimation of EEGs using the multivariate AR, AR-MA, and conventional windowed periodogram analysis are compared.

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