# Inferring neural sources from electroencephalography: foundations and frontiers

## TL;DR

This paper reviews methods for improving the accuracy of brain activity localization using EEG, focusing on overcoming spatial resolution limitations and integrating advanced techniques.

## Contribution

The paper synthesizes recent advances in nonlinear inverse modeling and multimodal integration for EEG source localization.

## Key findings

- Nonlinear methods and high-density EEG systems can better address anatomical variability and source complexity.
- Multimodal integration improves the accuracy of neural source estimation.
- Publicly available datasets and software tools are essential for advancing EEG source localization research.

## Abstract

Electroencephalography (EEG) provides robust, cost-effective, and portable measurements of brain electrical activity. However, its spatial resolution is limited, constraining the localization and estimation of deep sources. Although methods exist to infer neural activity from scalp recordings, major challenges remain due to high dimensionality, temporal overlap among neural sources, and anatomical variability in head geometry. This topical review synthesizes inverse modeling approaches, with emphasis on nonlinear methods, multimodal integration, and high-density EEG systems that address these limitations. We also review the forward model and related background theory, summarize clinical applications, outline research directions, and identify available software tools and relevant publicly available datasets. Our goal is to help researchers understand traditional source estimation techniques and integrate advanced methods that may better capture the complexity of neurophysiological sources.