Pole-zero spectral modeling of EEG

Abstract

This paper deals with the pole-zero spectral modeling of EEG by the methods based on the combination of homomorphic filtering and linear prediction. The homomorphic prediction method, which models the minimum phase equivalent of the signal, and the pole-zero decomposition method which models pole and zero spectra separately, are considered. These two methods yield similar performance for EEG. However, the latter has an edge over the former in providing a better spectral fit to the log magnitude spectrum, specifically with respect to valley region and the bandwidth of the spectral peak. The direct pole-zero modeling of EEG (without homomorphic filtering) has been found to affect the zero spectrum severely, resulting in an inaccurate spectral estimate. The zero estimates by the direct method, at least in some EEGs, indicate that in general, EEG is not a minimum phase signal. Even in cases where there is no indication of mixed phase nature of EEG, the spectral estimates obtained by the proposed methods are found to be far superior to the direct method. Furthermore, on comparison for the same number of parameters, these two methods are found to perform better than the Burg's all-pole modeling. The study is based on real EEGs recorded from three different subjects.