Classification of Electroencephalogram using Artificial Neural Networks

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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A C Tsoi, D S C So, A Sergejew


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.