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Article Dans Une Revue PLoS ONE Année : 2013

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

Résumé

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.

Dates et versions

hal-04461548 , version 1 (16-02-2024)

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Vince Calhoun, Vamsi Potluru, Ronald Phlypo, Rogers Silva, Barak Pearlmutter, et al.. Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence. PLoS ONE, 2013, 8 (8), pp.e73309. ⟨10.1371/journal.pone.0073309⟩. ⟨hal-04461548⟩
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