Selection of a Subset of EEG Channels using PCA to classify Alcoholics and Non-alcoholics

The principal component analysis (PCA) is proposed as feature selection method in choosing a subset of channels for visual evoked potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics