Robust multiple external stimuli classification in functional MRI images

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Abstract

Functional magnetic resonance imaging (fMRI) is an effective imaging modality in analyzing neural activation of human brain to external stimuli. The general analysis process includes feature selection and classification. Feature selection can be limited to specific anatomical regions or be implemented by using univariate or multi-variate statistical methods. However, both of these introduce some limitations for decoding different brain states. This paper introduces a new hybrid univariate and multivariate feature selection scheme, and explores different feature selection methods, i.e. orthogonal matching pursuit (OMP), smoothed Io (SL0) and principal feature analysis (PFA). The selected feature voxels are then classified by support vector machine (SVM) to produce predictions of perceived stimuli. Experimental results on Haxby single subject multiple stimuli dataset show the high effectiveness of this new multivoxel pattern analysis (MVPA) method.

Original languageEnglish
Title of host publication2016 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
Pages78-81
Number of pages4
ISBN (Electronic)9781479923502
DOIs
StatePublished - 15 Jun 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: 13 Apr 201616 Apr 2016

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2016-June
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Country/TerritoryCzech Republic
CityPrague
Period13/04/1616/04/16

Keywords

  • fMRI
  • feature selection
  • sparse representation

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