Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy

Abstract

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects.

In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of

epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical,

psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such

controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal

representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared

and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task

and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from

individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where

classification scores of up to 100% were obtained with a linear discriminant classifier.