Robust Principal Component Analysis for improving cognitive brain states discrimination from fMRI
Conference Paper, Proceedings of 6th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA '13), pp. 165 - 172, June, 2013
Abstract
In this paper we propose a new method to discriminate cognitive brain states directly from functional Magnetic Resonance Images (fMRI). First, we apply Robust Principal Component Analysis (RPCA) to construct low dimensional linear-subspace representations from the noisy fMRI images for each subject and then perform a Gaussian Naive Bayes (GNB) classification. In previous studies the discrimination of cognitive brain states from fMRI is done by transforming the fMRI into a time sequence of voxels from which the brain states are inferred. RPCA improved the classification rate of a real benchmark fMRI data.
BibTeX
@conference{Georgieva-2013-120900,author = {P. Georgieva and N. Nuntal and F. De la Torre},
title = {Robust Principal Component Analysis for improving cognitive brain states discrimination from fMRI},
booktitle = {Proceedings of 6th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA '13)},
year = {2013},
month = {June},
pages = {165 - 172},
}
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