Bayesian blind source separation for brain imaging

Hichem Snoussi, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper deals with the problem of blind source separation in fMRI data analysis. Our main contribution is to present a maximum likelihood based method to blindly separate the brain activations in an fMRI experiment. Choosing the time frequency domain as the signal representation space, our method relies on the second order statistics and exploits the intersource diversity. It is efficiently implemented by the EM (Expectation-Maximization) algorithm where the time courses of the brain activations are considered as the hidden variables. The estimation variance of the STFT (Short Time Fourier Transform) is reduced by averaging across time frequency sub-domains. The successful separation of the right and left visual cortex activations during a visual fMRI experiment, in a block design, and the extraction of only the relevant tasks corroborate the effectiveness of our proposed separating algorithm.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
Pages581-584
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: Sep 11 2005Sep 14 2005

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume3
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing 2005, ICIP 2005
CountryItaly
CityGenova
Period9/11/059/14/05

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Snoussi, H., & Calhoun, V. D. (2005). Bayesian blind source separation for brain imaging. In IEEE International Conference on Image Processing 2005, ICIP 2005 (pp. 581-584). [1530458] (Proceedings - International Conference on Image Processing, ICIP; Vol. 3). https://doi.org/10.1109/ICIP.2005.1530458