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Article: Independent component analysis involving autocorrelated sources with an application to functional magnetic resonance imaging

TitleIndependent component analysis involving autocorrelated sources with an application to functional magnetic resonance imaging
Authors
KeywordsTime series
Spectral analysis
Discrete Fourier transform
Blind source separation
Whittle likelihood
Issue Date2011
Citation
Journal of the American Statistical Association, 2011, v. 106, n. 495, p. 1009-1024 How to Cite?
AbstractIndependent component analysis (ICA) is an effective data-driven method for blind source separation. It has been successfully applied to separate source signals of interest from their mixtures. Most existing ICA procedures are carried out by relying solely on the estimation of the marginal density functions, either parametrically or nonparametrically. In many applications, correlation structures within each source also play an important role besides the marginal distributions. One important example is functional magnetic resonance imaging (fMRI) analysis where the brain-function-related signals are temporally correlated. In this article, we consider a novel approach to ICA that fully exploits the correlation structures within the source signals. Specifically, we propose to estimate the spectral density functions of the source signals instead of their marginal density functions. This is made possible by virtue of the intrinsic relationship between the (unobserved) sources and the (observed) mixed signals. Our methodology is described and implemented using spectral density functions from frequently used time series models such as autoregressive moving average (ARMA) processes. The time series parameters and the mixing matrix are estimated via maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through extensive simulation studies and a real fMRI application. The numerical results indicate that our approach outperforms several popular methods including the most widely used fastICA algorithm. This article has supplementary material online. © 2011 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/219654
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447

 

DC FieldValueLanguage
dc.contributor.authorLee, Seonjoo-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorTruong, Young-
dc.contributor.authorLewis, Mechelle-
dc.contributor.authorHuang, Xuemei-
dc.date.accessioned2015-09-23T02:57:38Z-
dc.date.available2015-09-23T02:57:38Z-
dc.date.issued2011-
dc.identifier.citationJournal of the American Statistical Association, 2011, v. 106, n. 495, p. 1009-1024-
dc.identifier.issn0162-1459-
dc.identifier.urihttp://hdl.handle.net/10722/219654-
dc.description.abstractIndependent component analysis (ICA) is an effective data-driven method for blind source separation. It has been successfully applied to separate source signals of interest from their mixtures. Most existing ICA procedures are carried out by relying solely on the estimation of the marginal density functions, either parametrically or nonparametrically. In many applications, correlation structures within each source also play an important role besides the marginal distributions. One important example is functional magnetic resonance imaging (fMRI) analysis where the brain-function-related signals are temporally correlated. In this article, we consider a novel approach to ICA that fully exploits the correlation structures within the source signals. Specifically, we propose to estimate the spectral density functions of the source signals instead of their marginal density functions. This is made possible by virtue of the intrinsic relationship between the (unobserved) sources and the (observed) mixed signals. Our methodology is described and implemented using spectral density functions from frequently used time series models such as autoregressive moving average (ARMA) processes. The time series parameters and the mixing matrix are estimated via maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through extensive simulation studies and a real fMRI application. The numerical results indicate that our approach outperforms several popular methods including the most widely used fastICA algorithm. This article has supplementary material online. © 2011 American Statistical Association.-
dc.languageeng-
dc.relation.ispartofJournal of the American Statistical Association-
dc.subjectTime series-
dc.subjectSpectral analysis-
dc.subjectDiscrete Fourier transform-
dc.subjectBlind source separation-
dc.subjectWhittle likelihood-
dc.titleIndependent component analysis involving autocorrelated sources with an application to functional magnetic resonance imaging-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1198/jasa.2011.tm10332-
dc.identifier.scopuseid_2-s2.0-80054708838-
dc.identifier.volume106-
dc.identifier.issue495-
dc.identifier.spage1009-
dc.identifier.epage1024-

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