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Source-space ICA for EEG source separation, localization, and time-course reconstruction
Jonmohamadi, Yaqub Poudel, Govinda Innes, Carrie Jones, Richard
Jonmohamadi, Yaqub
Poudel, Govinda
Innes, Carrie
Jones, Richard
Abstract
We propose source-space independent component analysis (ICA) for separation, tomography, and time-course reconstruction of EEG and MEG source signals. Source-space ICA is based on the application of singular value decomposition and ICA on the neuroelectrical signals from all brain voxels obtained post minimum-variance beamforming of sensor-space EEG or MEG. We describe the theoretical background and equations, then evaluate the performance of this technique in several different situations, including weak sources, bilateral correlated sources, multiple sources, and cluster sources. In this approach, tomographic maps of sources are obtained by back-projection of the ICA mixing coefficients into the source-space (3-D brain template). The advantages of source-space ICA over the popular alternative approaches of sensor-space ICA together with dipole fitting and power mapping via minimum-variance beamforming are demonstrated. Simulated EEG data were produced by forward head modeling to project the simulated sources onto scalp sensors, then superimposed on real EEG background. To illustrate the application of source-space ICA to real EEG source reconstruction, we show the localization and time-course reconstruction of visual evoked potentials. Source-space ICA is superior to the minimum-variance beamforming in the reconstruction of multiple weak and strong sources, as ICA allows weak sources to be identified and reconstructed in the presence of stronger sources. Source-space ICA is also superior to sensor-space ICA on accuracy of localization of sources, as source-space ICA applies ICA to the time-courses of voxels reconstructed from minimum-variance beamforming on a 3D scanning grid and these time-courses are optimally unmixed via the beamformer. Each component identified by source-space ICA has its own tomographic map which shows the extent to which each voxel has contributed to that component.
Keywords
beamformer, EEG, independent component analysis, localization, time-course reconstruction
Date
2014
Type
Journal article
Journal
NeuroImage
Book
Volume
101
Issue
November
Page Range
720-737
Article Number
ACU Department
Mary MacKillop Institute for Health Research
Faculty of Health Sciences
Faculty of Health Sciences
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Relation URI
Source URL
Event URL
Open Access Status
License
File Access
Controlled