title: [Constrained ICA and its application to removing artifacts in EEG].
abstract: Independent component analysis (ICA) is a statistic technique which extracts independent components from a set of standard signals. Since Electroencephalogram (EEG) signals are the mixture of several relatively independent sources, ICA has attracted extensive attention in the field of EEG processing. In this paper, a new Constrained ICA (cICA) algorithm is introduced, it would solve the problem of orderless output when FastICA algorithm is used. The experiment results testify that the cICA algorithm can reduce the effect of different individual when the artifacts of EEG are removed manually. The results also show that the cICA algorithm is robust and performs faster convergence.
authors.affiliation: Departmnent of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, Citina. eqsing@gmail.com
authors.forename: Ansheng
authors.initials: A
authors.lastname: Gao
authors.forename: Yangyu
authors.initials: Y
authors.lastname: Luo
authors.forename: Ken
authors.initials: K
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted