EEGIFT: Group Independent Component Analysis for Event-Related EEG Data

• Published in: Computational Intelligence and Neuroscience Vol. 2011; no. 2011; pp. 18 - 26
• Main Authors: Eichele, Tom, Calhoun, Vince D., Brakedal, Brage, Eikeland, Rune, Rachakonda, Srinivas
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2011; Hindawi Puplishing Corporation; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Algorithms; Computer Simulation; Data Interpretation, Statistical; Electroencephalography; Electroencephalography - statistics & numerical data; Electronic data processing; Evoked Potentials - physiology; Humans; Image Processing, Computer-Assisted; Models, Statistical; Principal Component Analysis - methods; Principal components analysis; Reference Values; Norway; United States
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2011/129365

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.