Modeling sparse connectivity between underlying brain sources for EEG/MEG

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.

9 pages, 6 figures

References in corpus (4)

• Robustly estimating the flow direction of information in complex physical systems
• Kernel method for nonlinear Granger causality
• Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
• Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction

Cited by in corpus (5)

• Causal Network Inference via Group Sparse Regularization
• Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model
• Validity of time reversal for testing Granger causality
• Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation
• Multi-Scale Factor Analysis of High-Dimensional Brain Signals