Localizing bicoherence from EEG and MEG

Shahbazi Avarvand, Forooz and Bartz, Sarah and Andreou, Christina and Samek, Wojciech and Leicht, Gregor and Mulert, Christoph and Engel, Andreas K. and Nolte, Guido. (2018) Localizing bicoherence from EEG and MEG. NeuroImage, 174. pp. 352-363.

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Official URL: https://edoc.unibas.ch/79936/

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We propose a new method for the localization of nonlinear cross-frequency coupling in EEG and MEG data analysis, based on the estimation of bicoherences at the source level. While for the analysis of rhythmic brain activity, source directions are commonly chosen to maximize power, we suggest to maximize bicoherence instead. The resulting nonlinear cost function can be minimized effectively using a gradient approach. We argue, that bicoherence is also a generally useful tool to analyze phase-amplitude coupling (PAC), by deriving formal relations between PAC and bispectra. This is illustrated in simulated and empirical LFP data. The localization method is applied to EEG resting state data, where the most prominent bicoherence signatures originate from the occipital alpha rhythm and the mu rhythm. While the latter is hardly visible using power analysis, we observe clear bicoherence peaks in the high alpha range of sensorymotor areas. We additionally apply our method to resting-state data of subjects with schizophrenia and healthy controls and observe significant bicoherence differences in motor areas which could not be found from analyzing power differences.
Faculties and Departments:03 Faculty of Medicine > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Erwachsenenpsychiatrie (Riecher-Rössler)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Erwachsenenpsychiatrie (Riecher-Rössler)
UniBasel Contributors:Andreou, Christina
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:22 Dec 2020 10:03
Deposited On:22 Dec 2020 10:03

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