Unmixing fMRI with Independent Component Analysis Using ICA to Characterize High-Dimensional fMRI Data in a Concise Manner.

—Lord ByronIndependent component analysis (ICA) is a statistical methodused to discover hidden factors (sources or features) from a setof measurements or observed data such that the sources aremaximally independent. Typically, it assumes a generativemodel where observations are assumed to be linear mixturesof independent sources, and unlike principal component analy-sis (PCA), which uncorrelates the data, ICA works with high-er-order statistics to achieve independence. An intuitive example of ICA can be given by a scatter-plot oftwo independent signals

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