# Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings

## TL;DR

This paper presents a Bayesian method using sequential Monte Carlo to accurately localize multiple brain sources from combined EEG and MEG data, reducing uncertainty compared to single-modality analysis.

## Contribution

It introduces a novel Bayesian multi-dipole localization approach that effectively integrates EEG and MEG data for improved source estimation.

## Key findings

- Combined data improves localization of elusive sources.
- Posterior distribution variance is lower with combined data.
- Method successfully characterizes the number and locations of dipoles.

## Abstract

We deal with estimation of multiple dipoles from combined MEG and EEG time--series. We use a sequential Monte Carlo algorithm to characterize the posterior distribution of the number of dipoles and their locations. By considering three test cases, we show that using the combined data the method can localize sources that are not easily (or not at all) visible with either of the two individual data alone. In addition, the posterior distribution from combined data exhibits a lower variance, i.e. lower uncertainty, than the posterior from single device.