뇌파/뇌자도 전류원 국지화의 공간분해능 향상을 위한 독립성분분석 기반의 부분공간 탐색 알고리즘
An ICA-Based Subspace Scanning Algorithm to Enhance Spatial Resolution of EEG/MEG Source Localization
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초록
In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.
Abstract
In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.
참고문헌 (17)
- L.Ding, "Ictal source analysis:localization and imaging of causal interactions in humans.", NeuroImage, 34, pp.575~586 (2007)
- I.Florindo, "Lateralizing value of the auditory aura in partial seizures.", Epilepsia, 47, pp.68~72 (2006)
- S.Baillet, "Electromagnetic Brain Mapping", IEEE Signal Process.Mag., 통권 : 11, pp.14~30 (2001)
- B. He, "Modeling and Imaging of Bioelectric Activity", pp.2004, Kluwer Academic
- J.Mosher, "Multiple dipole modeling and localization from spatio-temporal MEG data", IEEE Trans.Biomed.Eng., 39, pp.541~557 (1992)
- X.Xu, "An alternative subspace approach to EEG dipole source localization", Phys.Med.Biol., 49, pp.327~343 (2004)
- K.Kim, "Independent component analysis for synthetic aperture magnetometry in magnetocardiography", Comput Biol Med., 36, pp.253~261 (2006)
- M.Fuchs, "Boundary element method volume conductor models for EEG source reconstruction.", Clin.Neurophysiol., 112, pp.1400~1407 (2001)
- M.Hamalainen, "Magnetoencephalography-theory,instrumentation,and applications to noninvasive studies of the working human brain", Rev.Mod.Phys., 65, pp.413~497 (1993)
- J.P.Marques, "ICA decomposition of EEG signal for fMRI processing in epilepsy.", Hum.Brain Mapp., 30, pp.2986~2996 (2008)
- E.Urrestarazu, "Independent component analysis removing artifacts in ictal recordings", Epilepsia, 45, pp.1071~1078 (2004)
- S.Sanei, "Detection and localization of epileptic seizures using ICA", Proc.IEEE Sensor Array and Multichannel Signal Processing Workshop, pp.20~23 (2004)
- K.Y. Jung, "Source localization of periodic sharp wave complexes using independent component analysis in sporadic Creutzfeldt-Jakob disease", Brain Res., 1143, pp.228~237 (2007)
- J.S.Kim, "Localization and propagation analysis for ictal source rhythm by elctrocorticography" (2010), NeuroImage,in press
- J.Haueisen, "Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head", IEEE Trans.Biomed.Eng., 44, pp.727~735 (1997)
- J.S.Kim, "Localization and propagation analysis for ictal source rhythm by elctrocorticography", NeuroImage, 52, pp.1279~1288 (2010)
- L.Ding, "Ictal source analysis:Localization and imaging of causal interactions in humans", NeuroImage, 34, pp.575~586 (2007)