title: [Eliminating artifacts of EEG data based on independent component analysis].
abstract: As a new array processing technique, independent component analysis(ICA) is an effective means to resolve the blind source separation(BSS) problem. Based on the brief introductions of ICA theory and algorithm, we apply ICA to the removal of ocular artifacts from EEG recordings. The EEG data collected from the human scalp is actually the mixtures of some independent components. It is coincident with the basic assumptions of ICA. Compared with the traditional methods of artifacts elimination, ICA, a kind of spatial filter, is not restricted by the case of spectrum overlapping, and it has a good reservation of useful detail signals. In addition, the inverse weight matrix of ICA can be used to reflect the topographic structure of different independent sources of EEG.
authors.affiliation: Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, China of Anhui University, Hefei 230039.
authors.forename: Fei
authors.initials: F
authors.lastname: Long
authors.forename: Xiaopei
authors.initials: X
authors.lastname: Wu
authors.forename: Ling
authors.initials: L
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography; D005133:Eye Movements / Q000502:physiology; D006801:Humans; D012815:Signal Processing, Computer-Assisted