Exploring the Performance of EEG Signal Classifiers for Alcoholism

Abstract

Alcoholism is a tendency to continually rely on alcohol. Unchecked ingestion leads to gradual deteriorating mental health of the abusers. To study the changes in brain activity, electroencephalography (EEG) is one of the acute and low-cost methods. In this study, different sampling rates are experimented on the input EEG signals. The most favorable sampling rate is applied to extract features using the statistical parameters such as mean, median, variance and standard deviation. The best result obtained is an accuracy of 96.84% with support vector machine (SVM) classifier for a sampling rate of 16 by combining all the features extracted using aforementioned statistical parameters.