Localizing bicoherence from EEG and MEG

Forooz Shahbazi Avarvand; Sarah Bartz; Christina Andreou; Wojciech Samek; Gregor Leicht; Christoph Mulert; Andreas K Engel; Guido Nolte

doi:10.1016/j.neuroimage.2018.01.044

Localizing bicoherence from EEG and MEG

Standard

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, Jahrgang 174, 01.07.2018, S. 352-363.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung

Harvard

Shahbazi Avarvand, F, Bartz, S, Andreou, C, Samek, W, Leicht, G, Mulert, C, Engel, AK & Nolte, G 2018, 'Localizing bicoherence from EEG and MEG', NEUROIMAGE, Jg. 174, S. 352-363. https://doi.org/10.1016/j.neuroimage.2018.01.044

APA

Shahbazi Avarvand, F., Bartz, S., Andreou, C., Samek, W., Leicht, G., Mulert, C., Engel, A. K., & Nolte, G. (2018). Localizing bicoherence from EEG and MEG. NEUROIMAGE, 174, 352-363. https://doi.org/10.1016/j.neuroimage.2018.01.044

Vancouver

Shahbazi Avarvand F, Bartz S, Andreou C, Samek W, Leicht G, Mulert C et al. Localizing bicoherence from EEG and MEG. NEUROIMAGE. 2018 Jul 1;174:352-363. https://doi.org/10.1016/j.neuroimage.2018.01.044

Bibtex

@article{0489997b586b4ee294a35b8295623aad,

title = "Localizing bicoherence from EEG and MEG",

abstract = "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.",

keywords = "Journal Article",

author = "{Shahbazi Avarvand}, Forooz and Sarah Bartz and Christina Andreou and Wojciech Samek and Gregor Leicht and Christoph Mulert and Engel, {Andreas K} and Guido Nolte",

year = "2018",

month = jul,

day = "1",

doi = "10.1016/j.neuroimage.2018.01.044",

language = "English",

volume = "174",

pages = "352--363",

journal = "NEUROIMAGE",

issn = "1053-8119",

publisher = "Academic Press",

}

RIS

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

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 -