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Proceedings Paper

Feature transformation of neural activity with sparse and low-rank decomposition
Author(s): Kang-Yu Ni; James Benvenuto; Rajan Bhattacharyya; Rachel Millin
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Paper Abstract

We propose a novel application of the sparse and low-rank (SLR) decomposition method to decode cognitive states for concept activity measured using fMRI BOLD. Current decoding methods attempt to reduce the dimensionality of fMRI BOLD signals to increase classification rate, but do not address the separable issues of multiple noise sources and complexity in the underlying data. Our feature transformation method extends SLR to separate task activity from the resting state and extract concept specific cognitive state. We show a significant increase in single trial decoding of concepts from fMRI BOLD using SLR to extract task specific cognitive state.

Paper Details

Date Published: 17 March 2015
PDF: 9 pages
Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 94172B (17 March 2015); doi: 10.1117/12.2081259
Show Author Affiliations
Kang-Yu Ni, HRL Labs., LLC (United States)
James Benvenuto, HRL Labs., LLC (United States)
Rajan Bhattacharyya, HRL Labs., LLC (United States)
Rachel Millin, HRL Labs., LLC (United States)
The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9417:
Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Robert C. Molthen, Editor(s)

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