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TNN
2010

Behavior-constrained support vector machines for fMRI data analysis

9 years 6 days ago
Behavior-constrained support vector machines for fMRI data analysis
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.
Danmei Chen, Sheng Li, Zoe Kourtzi, Si Wu
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TNN
Authors Danmei Chen, Sheng Li, Zoe Kourtzi, Si Wu
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