Modeling sparse connectivity between underlying brain sources for EEG/MEG

TL;DR

This paper introduces SCSA, a novel method for assessing brain connectivity in EEG/MEG signals that models neural sources with sparsity to overcome volume conduction issues, validated through simulations.

Contribution

The paper presents SCSA, a new technique combining MVAR modeling and Group Lasso to accurately estimate sparse brain source connectivity from EEG/MEG data.

Findings

SCSA outperforms existing algorithms in simulated data tests.

The method effectively reduces volume conduction effects.

SCSA provides a data-driven sparse connectivity model.

Abstract

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.