Complex ICA of brain imaging data

Tülay Adali, Vince Daniel Calhoun

Research output: Contribution to journalArticle

Abstract

The independent component analysis (ICA) is a data-centric approach that provides a more flexible framework for the analysis of functional magnetic resonance imaging (fMRI), a tool utilized in both research and clinical arenas. The ICA facilitates the analysis of fMRI data in its complex form by eliminating the need to explicitly model the phase behavior. The ICA has been successfully used in fMRI applications that proved challenging for the regression-type approaches. Such applications are identification of various signal types in the spatial or temporal domain, analysis of multi-subject fMRI data and finally, the analysis of complex-valued fMRI data. The main advantage of using ICA is the ability to model cognitive processes for which detailed a priori models of brain.

Original languageEnglish (US)
Pages (from-to)136-139
Number of pages4
JournalIEEE Signal Processing Magazine
Volume24
Issue number5
DOIs
StatePublished - 2007
Externally publishedYes

Fingerprint

Functional Magnetic Resonance Imaging
Complex Analysis
Independent component analysis
Independent Component Analysis
Brain
Imaging
Imaging techniques
Domain Analysis
Cognitive Models
Phase behavior
Regression
Magnetic Resonance Imaging
Model

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Complex ICA of brain imaging data. / Adali, Tülay; Calhoun, Vince Daniel.

In: IEEE Signal Processing Magazine, Vol. 24, No. 5, 2007, p. 136-139.

Research output: Contribution to journalArticle

Adali, Tülay ; Calhoun, Vince Daniel. / Complex ICA of brain imaging data. In: IEEE Signal Processing Magazine. 2007 ; Vol. 24, No. 5. pp. 136-139.
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