TY - JOUR
T1 - Detecting synchrony in EEG
T2 - A comparative study of functional connectivity measures
AU - Bakhshayesh, Hanieh
AU - Fitzgibbon, Sean P.
AU - Janani, Azin S.
AU - Grummett, Tyler S.
AU - Pope, Kenneth J.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - 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.
AB - 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.
KW - Biomedical signal processing
KW - Connectivity
KW - EEG
KW - Nonstationarity
UR - http://www.scopus.com/inward/record.url?scp=85058363332&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2018.12.005
DO - 10.1016/j.compbiomed.2018.12.005
M3 - Article
C2 - 30562626
AN - SCOPUS:85058363332
VL - 105
SP - 1
EP - 15
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
SN - 0010-4825
ER -