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In this paper, a wavelet-based neural network (WNN) classifier forrecognizing EEG signals is implemented and tested under three sets EEG signals(healthy subjects, patients with epilepsy and patients with epileptic syndromeduring the seizure). First, the Discrete Wavelet Transform (DWT) with theMulti-Resolution Analysis (MRA) is applied to decompose EEG signal atresolution levels of the components of the EEG signal (delta, theta, alpha,beta and gamma) and the Parsevals theorem are employed to extract thepercentage distribution of energy features of the EEG signal at differentresolution levels. Second, the neural network (NN) classifies these extractedfeatures to identify the EEGs type according to the percentage distribution ofenergy features. The performance of the proposed algorithm has been evaluatedusing in total 300 EEG signals. The results showed that the proposed classifierhas the ability of recognizing and classifying EEG signals efficiently.

Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier