|Chapter title||EEG signal classification using wavelet feature extraction and neural networks|
|Authors||Jahankhani, P., Kodogiannis, V. and Revett, K.|
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.
|Keywords||Biology computing, decision making, electroencephalography, feature extraction, learning (artificial intelligence), neural nets, signal classification, wavelet transforms, EEG signal classification, decision making, decision support system, electroencephalogram signal, incomplete data handling, learning-based algorithm classifier, missing data handling, neural network model, wavelet feature extraction, wavelet transform|
|Book title||JVA '06. IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, 2006.|
|Place of publication||Los Alamitos, USA|
|Digital Object Identifier (DOI)||https://doi.org/10.1109/JVA.2006.17|