Classification of Alcoholics and Non-Alcoholics via EEG Using SVM and Neural Networks

The alcoholism is one of psychiatric phenotype, which results from interplay between genetic and environmental factors. Not only it leads to brain defects but also associated cognitive, emotional, and behavioral impairments. It can be detected by analyzing EEG signals. In this research, the power spectrum of the Haar mother wavelet is extracted as features. Then the principle component analysis is applied for dimension reduction of the feature vectors. Finally support vectors machine and neural networks are used for classification. The simulation results show that our proposed method achieves better classification accuracy than the other methods.