Independent Component Analysis and Its Application in fMRI
Shuqian Luo · Acta Simulata Systematica Sinica · 2001
A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is finding a useful part from multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is Independent Component Analysis (ICA), in which the desired representation is the one that maximized the dependence of the component. This paper describes the basic concepts, method of ICA and its application in fMRI images. With ICA,random noise and physiological interferences such as heart beats,respiration,are suppressed effectively, and functional signals of the image are enhanced.