Localizing bicoherence from EEG and MEG
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Localizing bicoherence from EEG and MEG. / Shahbazi Avarvand, Forooz; Bartz, Sarah; Andreou, Christina; Samek, Wojciech; Leicht, Gregor; Mulert, Christoph; Engel, Andreas K; Nolte, Guido.
In: NEUROIMAGE, Vol. 174, 01.07.2018, p. 352-363.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research
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TY - JOUR
T1 - Localizing bicoherence from EEG and MEG
AU - Shahbazi Avarvand, Forooz
AU - Bartz, Sarah
AU - Andreou, Christina
AU - Samek, Wojciech
AU - Leicht, Gregor
AU - Mulert, Christoph
AU - Engel, Andreas K
AU - Nolte, Guido
N1 - Copyright © 2018 Elsevier Inc. All rights reserved.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
KW - Journal Article
U2 - 10.1016/j.neuroimage.2018.01.044
DO - 10.1016/j.neuroimage.2018.01.044
M3 - SCORING: Journal article
C2 - 29421325
VL - 174
SP - 352
EP - 363
JO - NEUROIMAGE
JF - NEUROIMAGE
SN - 1053-8119
ER -