EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Authors:
Pari Jahankhani;Vassilis Kodogiannis;Kenneth Revett
Affiliations:
Univ. of Westminster, London, UK;Univ. of Westminster, London, UK;Univ. of Westminster, London, UK
Venue:
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
Year:
2006

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Abstract

Decision Support Systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.