Classification of Electroencephalogram using Artificial Neural Networks

In this paper, we will consider the problem of classifying electroencephalo(cid:173) gram (EEG) signals of normal subjects, and subjects suffering from psychi(cid:173) atric disorder, e.g., obsessive compulsive disorder, schizophrenia, using a class of artificial neural networks, viz., multi-layer perceptron. It is shown that the multilayer perceptron is capable of classifying unseen test EEG signals to a high degree of accuracy.