Implementing eigen features methods/neural network for EEG signal analysis

Abstract

This paper presented the possibility of implementing eigenvector methods to represent the features of electroencephalogram signal. In this study, three eigenvector methods were investigated namely Pisarenko, Multiple Signal Classification (MUSIC) and Modified Covariance. The ability of the features in representing good character of signal in order to discriminate two different EEG signals for relaxation and writing signal were tested using neural network. The power level obtained by eigenvector methods of the EEG signals were used as inputs of the neural network trained with Levenberg-Marquardt algorithm. The classification result shows that Modified Covariance method is a better technique to extract features for relaxation-writing task.