Classification of EEG signals using the wavelet transform

Abstract

This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. Three classes of EEG signals were used: normal, schizophrenia (SCH), and obsessive compulsive disorder (OCD). The architecture of the artificial neural network used in the classification is a three-layered feedforward network which implements the backpropagation of error learning algorithm. After training, the network with wavelet coefficients was able to correctly classify over 66% of the normal class and 71% of the schizophrenia class of EEGs. The wavelet transform thus provides a potentially powerful technique for preprocessing EEG signals prior to classification.