EEGIFT: Group Independent Component Analysis for Event-Related EEG Data. (20th May 2011)

• Record Type: Journal Article
• Authors: Eichele, Tom; Rachakonda, Srinivas; Brakedal, Brage; Eikeland, Rune; Calhoun, Vince D.
• Other Names: Baillet Sylvain Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2011(2011)
• Publication Date: 2011-05-20
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2011/129365
• Languages: English
• ISSNs: 1687-5265

Abstract

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.