  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.
