Separation of phase-locked sources in pseudo-real MEG data

Abstract

This article addresses the blind separation of linear mixtures of synchronous signals (i.e., signals with locked phases), which is a relevant problem, e.g., in the analysis of electrophysiological signals of the brain such as the electroencephalogram and the magnetoencephalogram (MEG). Popular separation techniques such as independent component analysis are not adequate for phase-locked signals, because such signals have strong mutual dependency. Aiming at unmixing this class of signals, we have recently introduced the independent phase analysis (IPA) algorithm, which can be used to separate synchronous sources. Here, we apply IPA to pseudo-real MEG data. The results show that this algorithm is able to separate phase-locked MEG sources in situations where the phase jitter (i.e., the deviation from the perfectly synchronized case) is moderate. This represents a significant step towards performing phase-based source separation on real data.

Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data

“…Several phase synchronization measures are less susceptible to common source effects (i.e., volume conduction and magnetic field spread). The common source effect is an inevitable and serious problem in electrophysiological studies (Vinck et al, 2011), and cannot be completely eliminated by source reconstruction methods (Schoffelen and Gross, 2009) or solved by independent component analysis (Almeida et al, 2013). The common source effect is problematic in estimating phase synchronization because it can generate spurious phase synchronization with a phase difference of 0 or 180 degrees (zero-lag) among channels or sensors (Cohen, 2014a;Khadem and Hossein-Zadeh, 2014).…”