Detecting Cognitive States from fMRI Images by Machine Learning and Multivariante Classification

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Departmental Papers (BE)
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Biomedical Engineering and Bioengineering
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The major obstacle in building classifiers that robustly detect a particular cognitive state across different subjects using fMRI images has been the high inter-subject functional variability in brain activation patterns. To overcome this obstacle, firstly, the brain regions that are relevant to the problem under study are determined from the training data; then, statistical information of each brain region is extracted to form regional features, which are robust to inter-subject functional variations within the brain region; finally, the regional feature statistical variations across different samples are further alleviated by a PCA technique. To improve the generalization ability and efficiency of the classification, from the extracted regional features, a hybrid feature selection method is utilized to select the most discriminative features, which are used to train a SVM classifier for decoding brain states from fMRI images. The performance of this method is validated in a deception fMRI study. The proposed method yielded better results compared to other commonly used fMRI image classification methods.

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2006-01-01
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2023-05-17T05:26:43.000
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Suggested Citation: Yong, F., D. Shen and C. Davatzikos. (2006). "Detecting Cognitive States from fMRI Images by Machine Learning and Multivariate Classification." Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop. 17-22 June 2006. ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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