Robust Evaluation and Comparison of EEG Source Localization Algorithms for Accurate Reconstruction of Deep Cortical Activity

Abstract

Accurately reconstructing deep cortical source activity from EEG recordings is essential for understanding cognitive processes. However, currently, there is a lack of reliable methods for assessing the performance of EEG source localization algorithms. This study establishes an algorithm evaluation framework, utilizing realistic human head models and simulated EEG source signals with spatial propagations. We compare the performance of several newly proposed Bayesian algorithms, including full Dugh, thin Dugh, and Mackay, against classical methods such as MN and eLORETA. Our results, which are based on 630 Monte Carlo simulations, demonstrate that thin Dugh and Mackay are mathematically sound and perform significantly better in spatial and temporal source reconstruction than classical algorithms. Mackay is less robust spatially, while thin Dugh performs best overall. Conversely, we show that full Dugh has significant theoretical flaws that negatively impact localization accuracy. This research highlights the advantages and limitations of various source localization algorithms, providing valuable insights for future development and refinement in EEG source localization methods.