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dc.contributor.authorBakhshayesh, Haniehen_US
dc.contributor.authorFitzgibbon, Sean Patricken_US
dc.contributor.authorJanani, Azinen_US
dc.contributor.authorGrummett, Tyler Sen_US
dc.contributor.authorPope, Kennethen_US
dc.date.accessioned2019-01-16T22:58:03Z
dc.date.available2019-01-16T22:58:03Z
dc.date.issued2018-12-11
dc.identifier.citationBakhshayesh, H., Fitzgibbon, S. P., Janani, A. S., Grummett, T. S., & Pope, K. J. (2019). Detecting synchrony in EEG: A comparative study of functional connectivity measures. Computers in Biology and Medicine, 105, 1–15. https:// doi.org/10.1016/j.compbiomed.2018.12.005en_US
dc.identifier.issn0010-4825
dc.identifier.urihttp://hdl.handle.net/2328/38831
dc.description© 2018 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 12 month embargo from date of publication (December 2018) in accordance with the publisher’s archiving policyen_US
dc.description.abstractIn 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.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2018 Elsevier Ltd.en_US
dc.subjectConnectivityen_US
dc.subjectEEGen_US
dc.subjectBiomedical signal processingen_US
dc.subjectNonstationarityen_US
dc.titleDetecting synchrony in EEG: A comparative study of functional connectivity measuresen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1016/j.compbiomed.2018.12.005en_US
dc.rights.holderElsevier Ltd.en_US
dc.rights.licenseCC-BY-NC-ND
local.contributor.authorOrcidLookupPope, Kenneth: https://orcid.org/0000-0002-5081-2723en_US


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