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Source Localization of EEG Brainwaves Activities via Mother Wavelets Families for SWT Decomposition.

Tarek Frikha1, Najmeddine Abdennour1, Faten Chaabane2

  • 1CES Lab, Université de Sfax, Sfax, Tunisia.

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|May 19, 2021
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Summary

This study compared 51 mother wavelets for electroencephalography (EEG) source localization in brain-computer interfaces (BCI). The sym20 mother wavelet was identified as the optimal choice for improved BCI accuracy.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-Computer Interfaces (BCI) utilize electroencephalography (EEG) signals to enable communication via brain activity.
  • EEG source reconstruction can enhance classification accuracy in BCI systems.
  • Accurate source localization of brain activity aids in recognizing cognitive states and neurological disorders.

Purpose of the Study:

  • To compare the effectiveness of 51 mother wavelets from 7 families for EEG source localization.
  • To identify the optimal mother wavelet for improving BCI accuracy.

Main Methods:

  • Stationary Wavelet Transform (SWT) decomposition of EEG signals using 51 mother wavelets.
  • Extraction of five brainwave subbands for source localization.
  • Independent Component Analysis (ICA) for feature extraction.
  • Boundary Element Model (BEM) and Equivalent Current Dipole (ECD) for forward and inverse solutions.

Main Results:

  • Evaluation of 51 mother wavelets across 7 wavelet families (Haar, Symlets, Daubechies, Coiflets, Discrete Meyer, Biorthogonal, reverse Biorthogonal).
  • Identification of sym20 as the superior mother wavelet for EEG source localization.
  • Bior6.8 and coif5 identified as secondary optimal wavelets.

Conclusions:

  • The sym20 mother wavelet demonstrates the highest efficacy for EEG source localization in BCI applications.
  • Optimal wavelet selection is crucial for enhancing the performance and accuracy of EEG-based brain-computer interfaces.
  • Further research can leverage these findings for advanced cognitive state recognition and neurological disorder diagnostics.