title: EEGIFT: group independent component analysis for event-related EEG data.
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.
authors: Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway. tom.eichele@gmail.com
authors: Tom
authors: T
authors: Eichele
authors: Srinivas
authors: S
authors: Rachakonda
authors: Brage
authors: B
authors: Brakedal
authors: Rune
authors: R
authors: Eikeland
authors: Vince D
authors: VD
authors: Calhoun
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D025341:Principal Component Analysis / Q000379:methods*; D012016:Reference Values