title: Delay correlation subspace decomposition algorithm and its application in fMRI.
abstract: This paper reports a new delay subspace decomposition (DSD) algorithm. Instead of using the canonical zero-delay correlation matrix, the new DSD algorithm introduces a delay into the correlation matrix of the subspace decomposition to suppress noises in the data. The algorithm is applied to functional magnetic resonance imaging (fMRI) to detect the regions of focal activities in the brain. The efficiency is evaluated by comparing with independent component analysis and principal component analysis method of fMRI.
authors.forename: Huafu
authors.initials: H
authors.lastname: Chen
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.forename: Lin
authors.initials: L
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013997:Time Factors