Analysis of EEG signals during relaxation and mental stress condition using AR modeling techniques

Electroencephalography (EEG) is the most important tool to study the brain behavior. This paper presents an integrated system for detecting brain changes during relax and mental stress condition. In most studies, which use quantitative EEG analysis, the properties of measured EEG are computed by applying power spectral density (PSD) estimation for selected representative EEG samples. The sample for which the PSD is calculated is assumed to be stationary. This work deals with a comparative study of the PSD obtained from resting and mental stress condition of EEG signals. The power density spectra were calculated using fast Fourier transform (FFT) by Welch's method, auto regressive (AR) method by Yule-Walker and Burg's method. Finally a neural network classifier used to classify these two conditions. It is found that maximum classification accuracy of 91.17% was obtained for the Burg Method compared to Yule Walker and Welch Method technique.