Comput Math Methods Med - Localizing true brain interactions from EEG and MEG data with subspace methods and modified beamformers.

Tópicos

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Resumo

To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method "RAP-MUSIC" to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a) RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b) conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c) the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.

Resumo Limpo

address problem mix eeg meg connect analysi exploit noninteract brain sourc contribut systemat imaginari part crossspectrum first propos appli exist subspac method rapmus subspac found domin singular vector imaginari part crossspectrum rather convent use covari matrix second estim specif sourc interact use modifi lcmvbeamform approach sourc direct voxel determin maxim imaginari coher respect given refer two method applic form number interact sourc even odddimension subspac collaps evendimension one simul show rapmus base imaginari part crossspectrum accur find correct sourc locat b convent rapmus fail sinc high influenc noninteract sourc c second method correct identifi sourc interact refer method also appli real data motor paradigm result local four interact sourc presum sensorymotor area

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