• Jarkko Ylipaavalniemi and Ricardo Vigário.
• Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland. jarkko.ylipaavalniemi@tkk.fi
• Neuroimage. 2008 Jan 1; 39 (1): 169-80.

AbstractIndependent component analysis (ICA) is a powerful data-driven signal processing technique. It has proved to be helpful in, e.g., biomedicine, telecommunication, finance and machine vision. Yet, some problems persist in its wider use. One concern is the reliability of solutions found with ICA algorithms, resulting from the stochastic changes each time the analysis is performed. The consistency of the solutions can be analyzed by clustering solutions from multiple runs of bootstrapped ICA. Related methods have been recently published either for analyzing algorithmic stability or reducing the variability. The presented approach targets the extraction of additional information related to the independent components, by focusing on the nature of the variability. Practical implications are illustrated through a functional magnetic resonance imaging (fMRI) experiment.

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