A simple approach to detect alcoholics using electroencephalographic signals

Electroencephalography (EEG) is a medical imaging technique that reads electrical activities generated by brain. In this study, electroencephalographic (EEG) signals acquired from alcoholics and controls have been analyzed. Raw EEG signals have 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 specify the eight channel pairs with highest relative entropies and lowest mutual informations in rank from standart 10-20 electrode system (19 channels). Five channels which gives higher accuracy for classification of alcoholics and controls have been extracted. 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. Skewness and kurtosis probability distribution measures were used to extract training and test feature vectors and their classification performances were evaluated. At the end, to compare our results with a known feature extraction method, Common Spatial Patterns (CSP) algorithm were applied to the EEG data set to extract feature and results were compared with the methods used in this study. The results of the experimental analysis have been found satisfactory for alcoholic detection and may be useful in studying genetic predisposition to alcoholism.