Evaluation of inverse methods and head models for EEG source localization using a human skull phantom

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) is crucial for non-invasively mapping brain activity.
• Accurate EEG source localization relies heavily on appropriate head models and inverse methods.
• Evaluating the performance of different EEG source localization techniques under varying conditions is essential.

Purpose of the Study:

• To investigate the performance of EEG source localization methods using a real-skull phantom.
• To assess the impact of different head models on localization accuracy.
• To evaluate the robustness of distributed source models and regularization schemes.

Main Methods:

• Experiments were conducted using a real-skull phantom filled with a conductive medium.
• Multiple current sources were positioned and oriented within the phantom to simulate brain activity.
• Various forward problem solutions (spherical to finite element method) and inverse methods were evaluated.
• Cortex-based source models and different regularization schemes were tested.

Main Results:

• Inverse methods employing cortex-based source models achieved high precision in locating active sources.
• Methods demonstrated minimal spurious activity, even with simultaneous dual sources.
• Advanced regularization techniques significantly improved the estimation of sparse and focal brain activity zones.
• Realistic head models were necessary for fitting the experimental data with low residual variance.

Conclusions:

• Cortex-based EEG source localization models offer excellent precision and robustness.
• Effective regularization schemes enhance the accuracy of identifying focal brain activity.
• While simplified models can be used, realistic head models are vital for optimal EEG data fitting and accurate source localization.