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Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic applications generally focus on the spectral content of EEG, which is the type of neural oscillations that can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and motivates the need for advanced signal processing techniques to aid clinicians in their interpretations. This paper describes the application of Wavelet Transform (WT) for the processing of Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for feature selection and dimensionality reduction where the informative and discriminative two-dimension features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP) neural network. For five classification problems, the proposed model achieves a high sensitivity, specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed methods and those obtained with previous literature methods shows the superiority of our approach for EEG signals classification and automated diagnosis.

Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.

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