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Proceedings Paper

Data partitioning and independent component analysis techniques applied to fMRI
Author(s): Axel Wismueller; Anke Meyer-Base; Oliver Lange; Thomas Dan Otto; Dorothee Auer
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Paper Abstract

Exploratory data-driven methods such as data partitioning techniques 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 data partitioning techniques and ICA in a systematic fMRI study. The comparative results were evaluated by (1) task-related activation maps and (2) associated time-courses. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, SOM, “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 better than the clustering methods but are limited to the linear mixture assumption. The data partitioning techniques outperform ICA in terms of classification results but requires a longer processing time than the ICA methods.

Paper Details

Date Published: 12 April 2004
PDF: 12 pages
Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004); doi: 10.1117/12.542219

Published in SPIE Proceedings Vol. 5439:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II
Harold H. Szu; Mladen V. Wickerhauser; Barak A. Pearlmutter; Wim Sweldens, Editor(s)

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