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

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DOI: 10.4236/jbise.2010.32026                

Abstract

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.

Keywords

Electroencephalogram (EEG); Wavelet Packet Transform (WPT); Support Vector Machine (SVM); Radial Basis Function Neural Network (RBFNN); Multilayer Back Propagation Neural Network (MLP-BPNN); Brain Computer Interface (BCI)

Conflicts of Interest

The authors declare no conflicts of interest.

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