Inferring neural sources from electroencephalography: foundations and frontiers

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.