Journal
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
Volume 8, Issue 3, Pages 387-398Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITB.2004.834406
Keywords
FastICA; functional magnetic resonance imaging (fMRI); Infomax; minimal free energy vector quantization (VQ); neural gas network; principal component analysis (PCA); self-organizing map; topographic independent component analysis; (ICA)
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Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, neural gas network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.
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