Object category classification of fMRI data using support vector machine combined with deactivation voxel selection

Caifeng Yan; Sutao Song; Yao Li; Xiaojuan Guo

doi:10.1117/12.910992

16 April 2012 Object category classification of fMRI data using support vector machine combined with deactivation voxel selection

Caifeng Yan, Sutao Song, Yao Li, Xiaojuan Guo

Author Affiliations +

Proceedings Volume 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging; 83171O (2012) https://doi.org/10.1117/12.910992
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Support Vector Machine (SVM) is an accurate pattern recognition method which has been widely used in functional MRI (fMRI) data classification. Voxel selection is a very important part in classification. In general, voxel selection is based on brain regions associated with activation caused by different experiment conditions or stimulations. However, negative blood oxygenation level-dependent responses (deactivation) which have also been found in humans or animals contribute to the classification of different cognitive tasks. Different from traditional studies which focused merely on the activation voxel selection methods, our aim is to investigate the deactivation voxel selection methods in the classification of fMRI data using SVM. In this study, three different voxel selection methods (deactivation, activation, the combination of deactivation and activation) are applied to decide which voxel is included in SVM classifier with linear kernel in classifying 4-category objects on fMRI data. The average accuracies of deactivation classification were 73.36%(house vs. face), 60.34%(house vs. car), 60.94%(house vs. cat), 71.43%(face vs. car), 63.17%(face vs. cat) and 61.61%(car vs. cat). The classification results of deactivation were significantly above the chance level which implies the deactivation is informative. The accuracies of combination of activation and deactivation method were close to that of activation method, and it was even better for some representative subjects. These results suggest deactivation provides useful information in the object category classification on fMRI data and the method of voxel selection based on both activation and deactivation will be a significant method in classification in the future.

Citation Download Citation

Caifeng Yan, Sutao Song, Yao Li, and Xiaojuan Guo "Object category classification of fMRI data using support vector machine combined with deactivation voxel selection", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 83171O (16 April 2012); https://doi.org/10.1117/12.910992

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Functional magnetic resonance imaging

Brain

Image classification

Blood

Data acquisition

Magnetic resonance imaging

Pattern recognition

Show All Keywords

Keywords/Phrases

Search In:

Publication Years