Modelling with independent components

Abstract

Independent Component Analysis (ICA) is a computational technique for identifying hidden statistically independent sources from multivariate data. In its basic form, ICA decomposes a 2D data matrix (e.g. time × voxels) into separate components that have distinct characteristics. In FMRI it is used to identify hidden FMRI signals (such as activations). Since… (More)
DOI: 10.1016/j.neuroimage.2012.02.020

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