Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks
This paper describes the application of rough sets and neural network models for classification of electroencephalogram (EEG) signals from two patient classes: normal and epileptic. First, the wavelet transform (WT) was applied to the EEG time series in order to reduce the dimensionality and highlight important features in the data. Statistical measures of the resulting wavelet coefficients were used for the classification task. Employing rough sets, we sought to determine which of the acquired attributes were necessary/informative as predictors of the decision classes. The results indicate that rough sets was able to accurately classify the datasets with an accuracy of almost 100%. The resulting rule sets were small, with an average cardinality of 6. These results were confirmed using standard neural network based classifiers.
KeywordsRadial Basis Function Discrete Wavelet Transform Hide Neuron Radial Basis Function Neural Network Recurrent Neural Network
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- 2.Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problems. In: Studies in Fuzziness and Soft Computing, vol. 56, pp. 49–88. Physica-Verlag, Heidelberg (2000)Google Scholar
- 4.Folkers, A., Mosch, F., Malina, T., Hofmann, U.G.: Realtime bioelectrical data acquisition and processing from 128 channels utilizing the wavelet-transformation. Neurocomputing, 52–54, pp. 247–254 (2003)Google Scholar
- 8.Ningler, M., Stockmanns, G., Schneider, G., Dressler, O., Kochs, E.F.: Rough Set-Based Classification of EEG-Signals to Detect Intraoperative Awareness: Comparison of Fuzzy and Crisp Discretization of Real Value Attributes. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 825–834. Springer, Heidelberg (2004)CrossRefGoogle Scholar