EEGIFT: Group Independent Component Analysis for Event-Related EEG Data
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. O...
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| Published in | Computational Intelligence and Neuroscience Vol. 2011; no. 2011; pp. 18 - 26 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Limiteds
01.01.2011
Hindawi Puplishing Corporation Hindawi Publishing Corporation John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5265 1687-5273 1687-5273 |
| DOI | 10.1155/2011/129365 |
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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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Brakedal, Brage Rachakonda, Srinivas Eichele, Tom Calhoun, Vince D. Eikeland, Rune |
| AuthorAffiliation | 2 Mind Research Network, 1101 Yale Boulevard, N.E, Albuquerque, NM 87131, New Mexico, USA 3 Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, USA 1 Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway |
| AuthorAffiliation_xml | – name: 1 Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway – name: 3 Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, USA – name: 2 Mind Research Network, 1101 Yale Boulevard, N.E, Albuquerque, NM 87131, New Mexico, USA |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21747835$$D View this record in MEDLINE/PubMed |
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| Contributor | Brakedal, Brage Rachakonda, Srinivas Eichele, Tom Calhoun, Vince D Eikeland, Rune |
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| Copyright | Copyright © 2011 Tom Eichele et al. COPYRIGHT 2011 John Wiley & Sons, Inc. Copyright © 2011 Tom Eichele et al. 2011 |
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| Snippet | 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.... |
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| SubjectTerms | 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 |
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| Title | EEGIFT: Group Independent Component Analysis for Event-Related EEG Data |
| URI | https://www.airitilibrary.com/Article/Detail/P20160527002-201112-201702200012-201702200012-18-26 https://search.emarefa.net/detail/BIM-447984 https://dx.doi.org/10.1155/2011/129365 https://www.ncbi.nlm.nih.gov/pubmed/21747835 https://www.proquest.com/docview/876251022 https://pubmed.ncbi.nlm.nih.gov/PMC3130967 https://downloads.hindawi.com/journals/cin/2011/129365.pdf |
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