title: A new constrained independent component analysis method.
abstract: Constrained independent component analysis (cICA) is a general framework to incorporate a priori information from problem into the negentropy contrast function as constrained terms to form an augmented Lagrangian function. In this letter, a new improved algorithm for cICA is presented through the investigation of the inequality constraints, in which different closeness measurements are compared. The utility of our proposed algorithm is demonstrated by the experiments with synthetic data and electroencephalogram (EEG) data.
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.forename: Jian-Xun
authors.initials: JX
authors.lastname: Mi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis