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

TL;DR

This study compares 51 mother wavelets across seven families in stationary wavelet transform decomposition of EEG signals to identify the most effective wavelet for accurate brain source localization.

Contribution

It systematically evaluates multiple wavelet families and identifies the optimal mother wavelet for EEG source localization using SWT, ICA, and BEM/ECD methods.

Findings

Sym 20 mother wavelet is the most effective for source localization.

Bior 6.8 and Coif 5 are also highly effective.

The study provides a comprehensive comparison of wavelet families for EEG analysis.

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.