Source localization of the EEG human brainwaves activities via all the different mother wavelets families for stationary wavelet transform decomposition

Abstract

The source localization of the human brain activities is an important resource for the recognition of cognitive state, medical disorders and a better understanding of the brain in general. In this study, we have compared 51 mother wavelets from 7 different wavelet families in a Stationary Wavelet transform (SWT) decomposition of an EEG signal. This process includes Haar, Symlets, Daubechies, Coiflets, Discrete Meyer, Biorthogonal and reverse Biorthogonal wavelet families in extracting five different brainwave sub-bands for a source localization. For this process, we used the Independent Component Analysis (ICA) for feature extraction followed by the Boundary Element Model (BEM) and the Equivalent Current Dipole (ECD) for the forward and inverse problem solutions. The evaluation results in investigating the optimal mother wavelet for source localization eventually identified the sym 20 mother wavelet as the best choice followed by bior 6.8 and coif 5.