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
