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Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data
Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3793313/ https://www.ncbi.nlm.nih.gov/pubmed/24171045 http://dx.doi.org/10.1155/2013/645921 |
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author | Wang, Lijun Lei, Yu Zeng, Ying Tong, Li Yan, Bin |
author_facet | Wang, Lijun Lei, Yu Zeng, Ying Tong, Li Yan, Bin |
author_sort | Wang, Lijun |
collection | PubMed |
description | Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information. |
format | Online Article Text |
id | pubmed-3793313 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37933132013-10-29 Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data Wang, Lijun Lei, Yu Zeng, Ying Tong, Li Yan, Bin Comput Math Methods Med Research Article Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information. Hindawi Publishing Corporation 2013 2013-09-21 /pmc/articles/PMC3793313/ /pubmed/24171045 http://dx.doi.org/10.1155/2013/645921 Text en Copyright © 2013 Lijun Wang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Lijun Lei, Yu Zeng, Ying Tong, Li Yan, Bin Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data |
title | Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data |
title_full | Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data |
title_fullStr | Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data |
title_full_unstemmed | Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data |
title_short | Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data |
title_sort | principal feature analysis: a multivariate feature selection method for fmri data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3793313/ https://www.ncbi.nlm.nih.gov/pubmed/24171045 http://dx.doi.org/10.1155/2013/645921 |
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