General nonunitary constrained ICA and its application to complex-valued fMRI data

Pedro A. Rodriguez, Matthew Anderson, Vince D. Calhoun, Tülay Adali

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Constrained independent component analysis (C-ICA) algorithms provide an effective way to introduce prior information into the complex- and real-valued ICA framework. The work in this area has focus on adding constraints to the objective function of algorithms that assume a unitary demixing matrix. The unitary condition is required in order to decouple - isolate - the constraints applied for each individual source. This assumption limits the optimization space and, therefore, the separation performance of C-ICA algorithms. We generalize the existing C-ICA framework by using a novel decoupling method that preserves the larger optimization space for the demixing matrix. This framework allows for the constraining of either the sources or the mixing coefficients. A constrained version of the nonunitary entropy bound minimization algorithm is introduced and applied to actual complex-valued fMRI data. We show that constraining the mixing parameters using a temporal constraint improves the estimation of the spatial map and timecourses of task-related components.

Original languageEnglish (US)
Article number6960099
Pages (from-to)922-929
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number3
StatePublished - Mar 1 2015


  • Constrained ICA
  • decoupled
  • entropy bound
  • mutual information (MI)

ASJC Scopus subject areas

  • Biomedical Engineering


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