Order selection of the linear mixing model for complex-valued FMRI data

Wei Xiong, Yi Ou Li, Nicolle Correa, Xi Lin Li, Vince D. Calhoun, Tülay Adali

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Functional magnetic resonance imaging (fMRI) data are originally acquired as complex-valued images, which motivates the use of complex-valued data analysis methods. Due to the high dimension and high noise level of fMRI data, order selection and dimension reduction are important procedures for multivariate analysis methods such as independent component analysis (ICA). In this work, we develop a complex-valued order selection method to estimate the dimension of signal subspace using informationtheoretic criteria. To correct the effect of sample dependence to information-theoretic criteria, we develop a general entropy rate measure for complex Gaussian random process to calibrate the independent and identically distributed (i.i.d.) sampling scheme in the complex domain. We show the effectiveness of the approach for order selection on both simulated and actual fMRI data. A comparison between the results of order selection and ICA on real-valued and complexvalued fMRI data demonstrates that a fully complex analysis extracts more meaningful components about brain activation.

Original languageEnglish (US)
Pages (from-to)117-128
Number of pages12
JournalJournal of Signal Processing Systems
Issue number2
StatePublished - May 2012
Externally publishedYes


  • Complex-valued fMRI
  • Entropy rate
  • I.i.d. sampling
  • Linear mixing model
  • Order selection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture


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