A SIMPLE APPROACH TO DETECT ALCOHOLICS USING ELECTROENCEPHALOGRAPHIC SIGNALS

In this paper, we have analyzed electroencephalogram (EEG) acquired from alcoholics and controls. Raw EEG signal has been filtered with an 8-30 Hz bandpass filter. Normalization of EEG trials to a range [-1, 1] was performed to filtered EEG signal. We employed relative entropy and mutual information to select the optimal channel configuration which maximize classification of alcoholics and controls. From standard 10-20 electrode system (19 channels), five channels which are more active in classification of alcoholic and control were chosen. Feature vectors of training and test data were obtained by concatenating variances of these five channels. When relative entropy was used for channel selection, 80.33% accuracy was obtained with k-nearest neighbors classifier accompanied with Mahalanobis distance metric. And mutual information for channel selection process provided 82.33% accuracy with k-nearest neighbors classifier accompanied with Euclidean distance metric. The results of the experimental analysis are satisfactory for alcoholic detection and may be useful in studying genetic predisposition to alcoholism.