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Inferring neural sources from electroencephalography: foundations and frontiers.

Anderson Roy Phillips1, Yash Shashank Vakilna1,2, Dorsa E P Moghaddam1

  • 1Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, United States of America.

Journal of Neural Engineering
|January 27, 2026
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Summary
This summary is machine-generated.

Electroencephalography (EEG) offers affordable brain activity measurement but struggles with deep source localization. This review explores advanced inverse modeling and high-density EEG to improve spatial resolution for better neurophysiological insights.

Keywords:
EEG source imagingforward and inverse modelinghigh-density EEGmultimodal integrationneural signal processingsource localizationstereo EEG (sEEG)

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Focus on brain electrical activity measurement and source localization.

Background:

  • Electroencephalography (EEG) is a cost-effective, portable tool for measuring brain activity.
  • Current EEG methods face limitations in spatial resolution, hindering deep source localization.
  • Challenges include high dimensionality, overlapping neural signals, and anatomical variability.

Purpose of the Study:

  • To synthesize inverse modeling approaches for improved EEG source localization.
  • To highlight nonlinear methods, multimodal integration, and high-density EEG systems.
  • To provide researchers with an understanding of traditional and advanced source estimation techniques.

Main Methods:

  • Review of inverse modeling techniques for EEG source estimation.
  • Emphasis on nonlinear inverse methods and multimodal data integration.
  • Discussion of high-density EEG systems and their role in enhancing spatial resolution.

Main Results:

  • Advanced methods like nonlinear modeling and multimodal integration offer potential solutions to EEG's spatial resolution limitations.
  • High-density EEG systems can improve the accuracy of source localization.
  • The review synthesizes existing knowledge and points towards future research directions.

Conclusions:

  • Improved source localization in EEG is achievable through advanced inverse modeling and technological enhancements.
  • Integrating multimodal data and utilizing high-density EEG are key strategies.
  • This review aims to guide researchers in applying these advanced methods for better understanding of neurophysiological sources.