Towards detecting connectivity in EEG: A comparative study of effective and functional connectivity measures on simulated data

Author: Hanieh Bakhshayesh

  • Thesis download: available for open access on 11 Nov 2021.

Bakhshayesh, Hanieh, 2019 Towards detecting connectivity in EEG: A comparative study of effective and functional connectivity measures on simulated data, Flinders University, College of Science and Engineering

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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 thesis, we address the following

question: which measure is best able to detect connections between signals, in the challenging

situation of non-stationary and noisy data from nonlinear systems, like EEG?

The problem with using EEG is that we don’t known when a measure is giving the “right”

answer. Hence we choose to apply connectivity measures to simulated data that is similar to

EEG rather than EEG itself, so we always know the “right” answer.

We compare almost all of the most widely used or most promising measures, in total 26

functional connectivity measures and 20 effective connectivity measures. The performance of

functional connectivity measures is tested on simulated data from two systems: two coupled

Hénon maps; and two channels of simulated EEG. The performance of effective connectivity

measures is tested on simulated data from three systems: three coupled Hénon maps; a

multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and

simulated EEG. To determine whether connectivity is detected, surrogate data were generated

and analysed, and a threshold determined from the surrogate ensemble.

No measure performed best in all tested situations. In the comparison of the functional

connectivity measures, correlation and coherence performed best on stationary data with many

samples, in both high and low noise. S-estimator, correntropy coefficient, 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, in both high and low noise. Of these, correlation and S-estimator have execution

times that scale slower with the number of channels and the number of samples. In the

comparison of effective connectivity measures, the measures that model the data as MVAR

perform well when the data are drawn from that model. Frequency domain measures perform

well when the data have a clearly defined band of interest. When neither of these are true,

information theoretic measures perform well, as does Copula Granger causality.

Keywords: Biomedical signal processing,Connectivity, EEG,Nonstationarity

Subject: Science, Technology and Enterprise thesis

Thesis type: Doctor of Philosophy
Completed: 2019
School: College of Science and Engineering
Supervisor: Kenneth Pope