A three domain covariance framework for EEG/MEG data

TL;DR

This paper introduces a three-domain covariance model for EEG/MEG data that accounts for spatial, temporal, and trial-to-trial variations, improving noise estimation and analysis accuracy.

Contribution

The paper proposes a novel Kronecker product covariance framework for EEG/MEG data, with an iterative maximum likelihood estimation algorithm and validation through simulations and real data.

Findings

Effective covariance estimation in simulated data

Improved dipole localization accuracy

Insights into spontaneous EEG/MEG properties

Abstract

In this paper we introduce a covariance framework for the analysis of EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. We perform a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. Apart from that, we illustrate our method on real EEG and MEG data sets. The proposed covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed for accurate dipole localization, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, such as in combined EEG/fMRI experiments in which the correlation between EEG and fMRI signals is investigated.