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Evaluation of EEG pre-processing and source localization in ecological research.

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  • 1Artificial Intelligence and Cognitive Load Research Lab, Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland.

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Summary

Electroencephalography (EEG) source localization (SL) can accurately map brain activity without individual MRI scans. This study confirms that established methods produce neurophysiologically plausible results for naturalistic EEG data.

Keywords:
eLORETAecological settingselectroencephalographyinverse modelingpipelinesource imagingsource localization

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalography (EEG) source localization (SL) is crucial for understanding brain function in neurological and psychiatric disorders.
  • A key limitation is the requirement for subject-specific anatomical data (MRI), often unavailable in real-world research.
  • This study addresses the neurophysiological plausibility of SL using only EEG signals.

Purpose of the Study:

  • To investigate if established EEG source localization methods can yield neurophysiologically plausible activation patterns without subject-specific anatomical information.
  • To validate template-based SL techniques on naturalistic EEG datasets.
  • To assess the feasibility of SL in research settings lacking individual MRI data.

Main Methods:

  • An end-to-end pipeline integrating automatic pre-processing and eLORETA source estimation was developed.
  • A shared forward model using the ICBM 2009c Nonlinear Symmetric template and CerebrA atlas was employed.
  • Validation was performed on the Healthy Brain Network (HBN) and COGBCI datasets, comparing different cognitive states and workloads using permutation testing.

Main Results:

  • Significant differences in brain activation were observed between resting and video-watching states, with increased posterior activity during visual tasks.
  • Cognitive workload analysis demonstrated progressive activation increases in executive function regions with rising task difficulty.
  • Permutation testing confirmed that reconstructed source activations align with expected neurophysiological patterns.

Conclusions:

  • Established EEG source localization methods can generate neurophysiologically plausible results without subject-specific anatomical data.
  • Template-based source analysis is a viable approach for research where individual MRI is impractical.
  • The study highlights the strengths and limitations of applying SL to naturalistic EEG data.