Source-space ICA for EEG source separation, localization, and time-course reconstruction

Journal article


Jonmohamadi, Yaqub, Poudel, Govinda, Innes, Carrie and Jones, Richard. (2014) Source-space ICA for EEG source separation, localization, and time-course reconstruction. NeuroImage. 101(November), pp. 720 - 737. https://doi.org/10.1016/j.neuroimage.2014.07.052
AuthorsJonmohamadi, Yaqub, Poudel, Govinda, Innes, Carrie and 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.

Keywordsbeamformer; EEG; independent component analysis; localization; time-course reconstruction
Year2014
JournalNeuroImage
Journal citation101 (November), pp. 720 - 737
PublisherElsevier
ISSN1053-8119
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neuroimage.2014.07.052
Scopus EID2-s2.0-84907046644
Page range720 - 737
Research GroupMary MacKillop Institute for Health Research
Publisher's version
File Access Level
Controlled
Place of publicationUnited States of America
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