Improvement in EEG Source Imaging Accuracy by Means of Wavelet Packet Transform and Subspace Component Selection

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalograph source imaging (ESI) offers high temporal resolution of cortical brain activity.
• Existing ESI methods struggle with accuracy due to noise and irrelevant signals, leading to incongruent source localization.
• Accurate brain activity source localization is crucial for understanding neurological disorders.

Purpose of the Study:

• To present a novel ESI method, Wavelet Packet Electroencephalograph Source Imaging (WPESI), for improved brain activity localization.
• To enhance the accuracy of ESI by effectively filtering noise and source-irrelevant signals.
• To evaluate the performance of WPESI in simulations, evoked potential experiments, and epilepsy patient data.

Main Methods:

• EEG signals were decomposed using Wavelet Packet Transform (WPT) into subspace components.
• Subspace components related to brain sources were selected, and signals were reconstructed via WPT.
• A Boundary Element Model (BEM) from head MRI was used for inverse calculation to obtain cortical current density distribution.

Main Results:

• WPESI demonstrated superior localization accuracy compared to the original sLORETA (OESI) in simulations and VEP experiments.
• For epilepsy patients, WPESI accurately estimated activity sources, aligning with seizure onset zones.
• The WPESI approach is user-friendly and achieves high accuracy in EEG source imaging.

Conclusions:

• The WPESI method offers a significant advancement in EEG source imaging accuracy.
• WPESI shows strong potential for precisely localizing epileptogenic foci from scalp EEG data.
• This technique could improve diagnosis and treatment planning for neurological conditions like epilepsy.