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    Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

    Calhoun, Vince D. and Potluru, Vamsi K. and Phlypo, Ronald and Silva, Rogers F. and Pearlmutter, Barak A. and Caprihan, Arvind and Plis, Sergey M. and Adali, Tulay (2013) Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence. PLoS ONE, 8 (8). e73309-1-e73309-8. ISSN 1932-6203

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    A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources.

    Item Type: Article
    Additional Information: © 2013 Calhoun et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Keywords: Independent Component Analysis; Brain fMRI; Selection; Maximal Independence; ICA algorithms;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 6548
    Depositing User: Barak Pearlmutter
    Date Deposited: 09 Nov 2015 16:42
    Journal or Publication Title: PLoS ONE
    Publisher: Public Library of Science
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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