Performance comparison of three artificial neural network methods for classification of electroencephalograph signals of five mental tasks
Vijay Khare, Jayashree Santhosh, Sneh Anand, Manvir Bhatia
DOI: 10.4236/jbise.2010.32026   PDF    HTML     5,114 Downloads   10,654 Views   Citations


In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw Electroencephalograph (EEG) signal. The three classifiers namely used were Multilayer Back propagation Neural Network, Support Vector Machine and Radial Basis Function Neural Network. In MLP-BP NN five training methods used were a) Gradient Descent Back Propagation b) Levenberg-Marquardt c) Resilient Back Propagation d) Conjugate Learning Gradient Back Propagation and e) Gradient Descent Back Propagation with movementum.

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Khare, V. , Santhosh, J. , Anand, S. and Bhatia, M. (2010) Performance comparison of three artificial neural network methods for classification of electroencephalograph signals of five mental tasks. Journal of Biomedical Science and Engineering, 3, 200-205. doi: 10.4236/jbise.2010.32026.

Conflicts of Interest

The authors declare no conflicts of interest.


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