Classification of Electroencephalogram using Artificial Neural Networks
Ah Chung Tsoi, Damwon So, Alex A. Sergejew · Neural Information Processing Systems · 1993
In this paper, we will consider the problem of classifying electroencephalogram (EEG) signals of normal subjects, and subjects suffering from psychiatric 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.