EEGIFT: group independent component analysis for event-related EEG data

Authors:
Tom Eichele;Srinivas Rachakonda;Brage Brakedal;Rune Eikeland;Vince D. Calhoun
Affiliations:
Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway;Mind Research Network, Albuquerque, New Mexico;Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway;Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway;Mind Research Network, Albuquerque, New Mexico and Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque
Venue:
Computational Intelligence and Neuroscience - Special issue on academic software applications for electromagnetic brain mapping using MEG and EEG
Year:
2011

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Abstract

Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes.We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.