Performance evaluation of classifiers for detection of alcoholics using electroencephalograms (EEG)

This paper suggests an automated detection of alcoholics and controls using Electroencephalograms (EEG). EEG signals are recorded from healthy and alcoholic subjects during visual object recognition task. Threshold of 100 V is used to remove the eye blink artifact and the gamma sub band (30-50 Hz) is extracted using elliptic band pass filter of 6th order. Different classical (spectral entropy), model based (power spectral density (PSD) estimation by Burg autoregressive (AR) model and eigenvector PSD estimation methods (MUSIC) in frequency domain are used as feature selection parameters. Three classification models such as feed forward Back propagation neural network (BPNN), Probabilistic neural network (PNN) and Support Vector Machine (SVM) networks are used to classify alcoholics and control subjects. The performances of BPNN and PNN classifiers are evaluated using 10-fold cross validation and holdout cross validation is performed to evaluate SVM classifier. Results show that model based and eigenvector based feature extraction methods are more accurate and sensitive than the conventional method such as spectral entropy estimation method to discriminate alcoholics from healthy controls. The SVM and PNN classifiers perform better than the BPNN classifier in terms of classification accuracy and computational complexity. However to achieve high classification accuracy, SVM does not require either the preprocessing of the input signal as in BPNN classifier or the tuning of the spread factor as in PNN classifier. Hence among the three classifiers used in this work, SVM seems to be an optimal classifier for classifying alcoholics and healthy subjects.