Complex ICA of brain imaging data

Tülay Adali, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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
Issue number5
StatePublished - Sep 2007
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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