Evaluation of parametric methods in EEG signal analysis

Abstract

In this paper, a well designed database, considering statistical characteristics and including all types of Electroencephalogram (EEG) is built. 900 EEG segments, each with a short interval (1.024 sec) in this database are clustered into eight classes. Three tests of white noise for evaluating the efficiency of autoregressive (AR) and autoregressive-moving average (ARMA) models are proposed. The Akaike information criterion (AIC) is used for determining orders of AR and ARMA models. The AR model requires a higher model order (8.67 on the average) than the ARMA model (6.17 on the average). However, it is found that about 96% of the 900 segments can be efficiently represented by the AR model, and only about 78% of them can be efficiently represented by ARMA model. We therefore conclude that the AR model is preferred for estimating EEG signals.