ORCID
- Bojan Bozic: 0000-0002-4420-1029
- Luca Longo: 0000-0002-2718-5426
Abstract
Introduction: Electroencephalography (EEG) source localization (SL) has shown potential for various applications, from epilepsy and seizure focus localization to psychiatric disorder evaluation. However, questions remain about its neurophysiological plausibility in real-world settings where only EEG signals are available without subject-specific anatomical information. This study investigates whether established pre-processing and source localization methods can produce neurophysiologically plausible activation patterns when applied to naturalistic EEG data without structural magnetic resonance imaging (MRI) or digitized electrode positions. Methods: Proven methods are aggregated into an end-to-end pipeline that includes automatic pre-processing, eLORETA for source estimation, and a shared forward model derived from the ICBM 2009c Nonlinear Symmetric template and its corresponding CerebrA atlas. The pipeline is validated using two distinct datasets: the Healthy Brain Network (HBN) dataset comparing resting and naturalistic video-watching states and the multi-session and multi-task EEG cognitive dataset (COGBCI) comparing different cognitive workload levels. The validation approach focuses on whether the reconstructed source activations exhibit expected neurophysiological patterns via permutation testing. Results: Findings revealed significant differences between resting state and video-watching tasks, with greater activation in posterior regions during video-watching, consistent with known visual processing pathways. The cognitive workload analysis similarly showed progressive activation increases with task difficulty, mapping to regions associated with executive function. Discussion: These results prove that established source localization methods can produce neurophysiologically plausible activation patterns without subject-specific information, highlighting the strengths and limitations of applying these methods to mid-length naturalistic EEG data. This research demonstrates the viability of template-based source analysis for research settings where individual structural imaging is unavailable or impractical.
Keywords
ecological settings, electroencephalography, eLORETA, inverse modeling, pipeline, source imaging, source localization
DOI Link
Publication Date
2025-01-01
Publication Title
Frontiers in Neuroimaging
Volume
4
Deposit Date
2026-11-05
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Gomez-Tapia, C., Bozic, B., & Longo, L. (2025) 'Evaluation of EEG pre-processing and source localization in ecological research', Frontiers in Neuroimaging, 4. Available at: 10.3389/fnimg.2025.1479569