Localizing true brain interactions from EEG and MEG data with subspace methods and modified beamformers.
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Localizing true brain interactions from EEG and MEG data with subspace methods and modified beamformers. / Forooz, Shahbazi Avarvand; Ewald, Arne; Nolte, Guido.
In: COMPUT MATH METHOD M, Vol. 2012, 2012, p. 402341.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Localizing true brain interactions from EEG and MEG data with subspace methods and modified beamformers.
AU - Forooz, Shahbazi Avarvand
AU - Ewald, Arne
AU - Nolte, Guido
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Humans
KW - Signal Processing, Computer-Assisted
KW - Algorithms
KW - Models, Neurological
KW - Software
KW - Computer Simulation
KW - Fourier Analysis
KW - Electroencephalography/methods
KW - Magnetoencephalography/methods
KW - Brain Mapping/methods
KW - Electrophysiology/methods
KW - Brain/pathology/physiology/physiopathology
KW - Humans
KW - Signal Processing, Computer-Assisted
KW - Algorithms
KW - Models, Neurological
KW - Software
KW - Computer Simulation
KW - Fourier Analysis
KW - Electroencephalography/methods
KW - Magnetoencephalography/methods
KW - Brain Mapping/methods
KW - Electrophysiology/methods
KW - Brain/pathology/physiology/physiopathology
U2 - 10.1155/2012/402341
DO - 10.1155/2012/402341
M3 - SCORING: Journal article
VL - 2012
SP - 402341
JO - COMPUT MATH METHOD M
JF - COMPUT MATH METHOD M
SN - 1748-670X
ER -
