title: General nonunitary constrained ICA and its application to complex-valued fMRI data.
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.
authors.forename: Pedro A
authors.initials: PA
authors.lastname: Rodriguez
authors.forename: Matthew
authors.initials: M
authors.lastname: Anderson
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.forename: Tulay
authors.initials: T
authors.lastname: Adali
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012815:Signal Processing, Computer-Assisted