# Inverse Modeling for MEG/EEG data

## TL;DR

This paper reviews mathematical methods for reconstructing brain activity from MEG/EEG data, covering forward modeling, regularization, Bayesian inference, and applications in epilepsy surgery.

## Contribution

It provides a comprehensive overview of current inverse modeling techniques, classifies methods based on source models, and discusses their advantages and disadvantages.

## Key findings

- Comparison of regularization methods for inverse problems
- Application of Bayesian inference to MEG/EEG data
- Case study on pre-surgical evaluation of epileptic patients

## Abstract

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.