00000cab a22000003a 4500 UP-99796217609164125 Buklod 20231007234053.0 m |o d | ta 100219s xx d | ||r ||||| || DENGII eng Calhoun, V.D. Unmixing fMRI with independent component analysis. pp. 79-90 Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources and works with higher-order statistics to achieve independence. ICA has recently demonstrated considerable promise in characterizing functional magnetic resonance imaging (fMRI) data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. In this article, ICA is introduced and its application to fMRI data analysis is reviewed. Blind source separation. Brain function. fMRI data analysis. Functional magnetic resonance imaging. Higher-order statistics. Independent component analysis. Independent sources. Statistical method. IEEE Engineering in medicine and biology magazine 25, 2 (2006). FO UPD DENG-II Article