Suitable Mother Wavelet Selection for EEG Signals Analysis: Frequency Bands Decomposition and Discriminative Feature Selection

Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signalssuch as electroencephalogram (EEG). The aim of this study is to choose the best and suitable motherwavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs canbe used, but the best MWT is the one that conserves the quasi-totality of information of the original signalon 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 percentagerootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as theselection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitableMWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extractedfeatures, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that theselected feature vector represents the amount of changes in frequency distribution and carries most of thediscriminative and representative information about their classes. Then, this study can provide a referencefor the selection of a suitable MWT and discriminativefeatures.