Detecting synchrony in EEG: A comparative study of functional connectivity measures

Abstract

In neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG. This requires knowledge of the true relationship between signals, hence we compare 26 measures of functional connectivity on simulated data (unidirectionally coupled Hénon maps, and simulated EEG). To determine whether synchrony is detected, surrogate data were generated and analysed, and a threshold determined from the surrogate ensemble. No measure performed best in all tested situations. The correlation and coherence measures performed best on stationary data with many samples. S-estimator, correntropy, mean-phase coherence (Hilbert), mutual information (kernel), nonlinear interdependence (S) and nonlinear interdependence (N) performed most reliably on non-stationary data with small to medium window sizes. Of these, correlation and S-estimator have execution times that scale slower with the number of channels and the number of samples. Keywords-Connectivity, EEG, biomedical signal processing, nonstationarity. Highlights:• Extensive comparative study of 26 functional connectivity measures for EEG • Measures compared on simulated noisy and nonstationary data • Surrogates used to determine threshold for significant connectivity • 8 measures performed well, choice of best depends on the particular situation • Correlation coefficient and S-estimator measures performed best overall M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2 synchronisation means adjustment of frequencies of periodic self-sustained oscillators due to weak interaction" [2]. The concept of synchronisation has been generalised to the case of chaotic oscillatory systems with irregular behaviour. The study of synchronisation between signals from such systems has been a topic of increasing interest, and has found applications in areas such as laser dynamics, solid state physics, electronics, biology, medicine, communication and even economics. There has been wide-ranging research aimed at detecting underlying relationships (which may be nonlinear and/or nonstationary) in multi-output dynamic systems, to give useful insight into their spatio-temporal organisation [3]. Synchronisation can manifest itself in different ways, hence a large variety of measures have been proposed to quantify synchronisation between signals. Synchronisation can occur due to one source driving another, and in such situations there is a direction to the relationship. Functional connectivity measures are symmetric and so cannot detect a direction in a relationship.Effective connectivity measures are not symmetric, and do detect a direction. T...

Increase in Mutual Information During Interaction with the Environment Contributes to Perception

“…Mutual information between two distributions of variables, represented by the binary states of neurons, in pairs of circuits in a network gives a measure of strength of connection in the brain. In fact, the mutual information kernel has been shown to be a good measure of functional connectivity in non-stationary data, such as electroencephalograph (EEG) [ 28 ].…”

Increase in Mutual Information During Interaction of the Brain with Environment Contributes to Perception

“…In the brain the mutual information between two distributions of variables, represented by the binary states of neurons, in pairs of circuits in a network will give a measure of strength of connection. In fact, mutual information kernel has been shown to be a good measure of functional connectivity in non-stationary data, such as EEG [17].…”

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

“…Functional connectivity has been measured using several approaches, including phase synchronization measures (Cohen, 2014c), amplitude envelope correlation (Bruns et al, 2000), information theoretical approach (Roulston, 1999) and other methods (Wen et al, 2015;Bakhshayesh et al, 2019) in studies using electroencephalography (EEG) and magnetoencephalography (MEG). Among them, phase synchronization measures, which assess a degree of clustering of phase differences between signals, are widely used (Varela et al, 2001;van Diessen et al, 2015).…”

“…Other studies compared FC measures from the viewpoint of reproducibility (Colclough et al, 2016;Garcés et al, 2016), but more reproducible FC measures do not necessarily exhibit better performance for detecting FCs. In addition, in many of these studies (Dauwels et al, 2010;Mezeiová and Paluš, 2012;Ard et al, 2015;Colclough et al, 2016;Garcés et al, 2016;Lowet et al, 2016;Bakhshayesh et al, 2019), comparisons were made between different types of FC measures (e.g., amplitude envelope correlation and phase synchronization measures) or between FC measures and effective connectivity measures. However, because different types of FC measures have different functional roles (Mehrkanoon et al, 2014;Guggisberg et al, 2015), they should be regarded as complementary approaches.…”