# Data-driven cortical clustering to provide a family of plausible solutions to M/EEG inverse problem

## TL;DR

This paper introduces a data-driven cortical clustering method for the M/EEG inverse problem, generating multiple plausible brain activity configurations instead of a single solution, addressing the ill-posed nature of the problem.

## Contribution

It proposes a novel approach that finds multiple plausible cortical regions explaining M/EEG data, unlike traditional convex optimization methods that select only one solution.

## Key findings

- Multiple cortical configurations can explain the same M/EEG data.
- The method identifies diverse solutions with similar data fit but different sizes and positions.
- It provides a set of plausible solutions, enhancing understanding of the inverse problem.

## Abstract

The M/EEG inverse problem is ill-posed. Thus additional hypotheses are needed to constrain the solution space. In this work, we consider that brain activity which generates an M/EEG signal is a connected cortical region. We study the case when only one region is active at once. We show that even in this simple case several configurations can explain the data. As opposed to methods based on convex optimization which are forced to select one possible solution, we propose an approach which is able to find several "good" candidates - regions which are different in term of their sizes and/or positions but fit the data with similar accuracy.