Statistical Analysis of Spectral Entropy Features for the Detection of Alcoholics Based on Elecroencephalogram (EEG) Signals

Abstract

EEG happens to be an important tool for brain study providing a non- invasive and cost effective method to detect the effects of alcohol on the human brain. This paper highlights the importance of statistical analysis to determine the usefulness of spectral entropy features for discriminating alcoholics from healthy subjects. The open source EEG database consisting of 10 alcoholic and 10 control subjects recordings under visual stimulus is considered for the study. The EEG signal is preprocessed to remove eye blink artefact using independent component analysis (ICA) and the gamma sub band is extracted by using an elliptic band pass filter to obtain the visually evoked pattern (VEP) signal. The spectral entropy (SEN) coefficients are computed on all the 61 VEP signals of each subject. To obtain a statistical measure of SEN coefficients suitability for classifying the alcoholic EEG, ANOVA tests are performed. Results show that the test exhibits interesting observations in the form of p-value <0.05 (accepted significance level) for most of the channels and p-value >0.05 for the remaining channels. This study may help in identifying those significant channels (p<0.05) which contribute to the classification of both the groups.