Independent Component Analysis is becoming a popular exploratory method for
analysing complicated data such as that from functional magnetic resonance imaging
(FMRI) experiments. The goal of ICA is to express a set of random variables as linear
combinations of statistically independent component variables. This talk will descibe the
underlying generative model, different techniques for estimating the components,
probabilistic extensions to ICA in order to overcome the 'overfitting' problem and
possible generalisations to a higher order decomposition for the joint analysis of multiple
FMRI data sets. Examples of estimated artifactual component maps will be provided.
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