title: [A method based on independent component analysis for processing fMRI data].
abstract: Independent component analysis (ICA) is a new technique in statistical signal processing to extract independent components from multidimensional measurements of mixed signals. In this paper, for the processing of functional magnetic resonance imaging(fMRI) data, two signals of near voxels are used as the mixed signals and are separated by ICA. The correlation coefficients between the reference signal and the separated signals are calculated and those voxels whose correlation coefficients are greater than a threshold are considered to be the activated voxels by the stimulation, and so the functional localization of the stimulation is completed. The validity of the method was primarily proved by trial of real brain functional magnetic resonance imaging data.
authors.affiliation: Dept of Applied Math, University of Electronic Science and Technology of China, Chengdu 610054.
authors.forename: Huafu
authors.initials: H
authors.lastname: Chen
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
authors.forename: Ke
authors.initials: K
authors.lastname: Zhou
authors.forename: Tiangang
authors.initials: T
authors.lastname: Zhou
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhuo
authors.forename: Lin
authors.initials: L
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D010775:Photic Stimulation; D025341:Principal Component Analysis