Independent Component Analysis Involving Autocorrelated Sources With an Application to Functional Magnetic Resonance Imaging.

ABSTRACT: Independent component analysis (ICA) is an effective data-driven method for blind source separation. It has been successfully applied to separate source signals of interest from their mixtures. Most existing ICA procedures are carried out by relying solely on the estimation of the marginal density functions, either parametrically or nonparametrically. In many applications, correlation structures within each source also play an important role besides the marginal distributions. One important example is functional magnetic resonance imaging (fMRI) analysis where the brain-function-related signals are temporally correlated. In this article, we consider a novel approach to ICA that fully exploits the correlation structures within the source signals. Specifically, we propose to estimate the spe

SUBMITTER: Lee S 

PROVIDER: S-EPMC4979078 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature