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

Hao Shen and Yuguo Yu ()
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Hao Shen: Research Institute of Intelligent and Complex Systems, Fudan University, Shanghai 200433, China
Yuguo Yu: Research Institute of Intelligent and Complex Systems, Fudan University, Shanghai 200433, China

Mathematics, 2023, vol. 11, issue 11, 1-24

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.

Keywords: EEG source localization; electrical source imaging; computational modeling; human brain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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