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.
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Georgieva, P., Nuntal, N., De la Torre, F. (2013). Robust Principal Component Analysis for Improving Cognitive Brain States Discrimination from fMRI. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_19
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DOI: https://doi.org/10.1007/978-3-642-38628-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
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