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Decoding Multiple Subject fMRI Data using Manifold based Representation of Cognitive State Neural Signatures

by Accamma I. V, Suma H. N, Dakshayini M
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 15
Year of Publication: 2015
Authors: Accamma I. V, Suma H. N, Dakshayini M
10.5120/20224-2512

Accamma I. V, Suma H. N, Dakshayini M . Decoding Multiple Subject fMRI Data using Manifold based Representation of Cognitive State Neural Signatures. International Journal of Computer Applications. 115, 15 ( April 2015), 1-7. DOI=10.5120/20224-2512

@article{ 10.5120/20224-2512,
author = { Accamma I. V, Suma H. N, Dakshayini M },
title = { Decoding Multiple Subject fMRI Data using Manifold based Representation of Cognitive State Neural Signatures },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 15 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number15/20224-2512/ },
doi = { 10.5120/20224-2512 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:54:52.346349+05:30
%A Accamma I. V
%A Suma H. N
%A Dakshayini M
%T Decoding Multiple Subject fMRI Data using Manifold based Representation of Cognitive State Neural Signatures
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 15
%P 1-7
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mind reading or thought prediction is a promising application of functional neuroimaging studies. The emergence of functional magnetic resonance imaging (fMRI) has, in the last two decades given a boost to these studies. In order to improve the accuracy, predictability and repeatability of thought prediction, it is important to have a representation that can capture the nuances of fMRI activations with respect to a particular cognitive state. In this paper, the process of creating a geometrical representation of the activations using non-linear manifolds is described. Manifold learning brings out the geometry of the activated voxels in the fMRI image. It is shown that this kind of representation is able to give high accuracy in classification studies as compared to using activation profiles.

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Index Terms

Computer Science
Information Sciences

Keywords

fMRI classification multiple-subject manifold learning

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