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Calhoun, Vince D., Potluru, Vamsi K., Phlypo, Ronald, Silva, Rogers F., Pearlmutter, Barak A., Caprihan, Arvind, 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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Abstract
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 |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/6548 |
| 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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