Can spurious indications for phase synchronization due to superimposed signals be avoided?

TL;DR

This paper compares phase-based methods for estimating interactions between dynamical systems, highlighting their susceptibility to common sources and noise, with implications for analyzing brain signals.

Contribution

It provides a comparative analysis of phase coherence, unweighted, and weighted phase lag index methods, revealing their relative robustness and limitations in real and simulated data.

Findings

Weighted phase lag index is less affected by common sources.

Unweighted phase lag index has limitations in certain scenarios.

Methods differ in their ability to detect true interactions.

Abstract

We investigate the relative merit of phase-based methods---mean phase coherence, unweighted and weighted phase lag index---for estimating the strength of interactions between dynamical systems from empirical time series which are affected by common sources and noise. By numerically analyzing the interaction dynamics of coupled model systems, we compare these methods to each other with respect to their ability to distinguish between different levels of coupling for various simulated experimental situations. We complement our numerical studies by investigating consistency and temporal variations of the strength of interactions within and between brain regions using intracranial electroencephalographic recordings from an epilepsy patient. Our findings indicate that the unweighted and weighted phase lag index are less prone to the influence of common sources but that this advantage may lead to constrictions limiting the applicability of these methods.