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Title: Behavior-constrained support vector machines for fMRI data analysis
Authors: Chen, Danmei ×
Li, Shengqiao
Kourtzi, Zoe
Wu, Si #
Issue Date: Oct-2010
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Transactions on Neural Networks vol:21 issue:10 pages:1680-5
Article number: 10.1109/TNN.2010.2060353
Abstract: Statistical learning methods are emerging as a valuable tool for decoding information from neural imaging data. The noisy signal and the limited number of training patterns that are typically recorded from functional brain imaging experiments pose a challenge for the application of statistical learning methods in the analysis of brain data. To overcome this difficulty, we propose using prior knowledge based on the behavioral performance of human observers to enhance the training of support vector machines (SVMs). We collect behavioral responses from human observers performing a categorization task during functional magnetic resonance imaging scanning. We use the psychometric function generated based on the observers behavioral choices as a distance constraint for training an SVM. We call this method behavior-constrained SVM (BCSVM). Our findings confirm that BCSVM outperforms SVM consistently.
URI: 
ISSN: 1045-9227
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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