On ICA of complex-valued fMRI: Advantages and order selection

Wei Xiong, Yi Ou Li, Hualiang Li, Tülay Adali, Vince D. Calhoun

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

Abstract

Functional magnetic resonance imaging (fMRI) data are originally acquired as complex-valued images, while virtually all fMRI studies only use the magnitude of the data in the analysis. Since little is known for devising models for the phase, independent component analysis (ICA) emerges as a promising technique for data-driven analysis of fMRI data in its native complex form. In this paper, we compare the performance of ICA on real-valued and complex-valued fMRI data and show the advantages of the complex approach. We also develop complex-valued order selection scheme to improve the estimation of the number of independent components in complex-valued fMRI data using information-theoretic criteria. Comparisons on order selection using real-valued and complex-valued fMRI data demonstrate the more informative nature of complex data.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages529-532
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Complex analysis
  • ICA
  • Order selection
  • fMRI

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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