Abstract
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 language | English (US) |
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Article number | 6960099 |
Pages (from-to) | 922-929 |
Number of pages | 8 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 62 |
Issue number | 3 |
DOIs | |
State | Published - Mar 1 2015 |
Keywords
- Constrained ICA
- decoupled
- entropy bound
- mutual information (MI)
ASJC Scopus subject areas
- Biomedical Engineering