Behavior-constrained support vector machines for fMRI data analysis

Authors:
Danmei Chen;Sheng Li;Zoe Kourtzi;Si Wu
Affiliations:
Department of Informatics, University of Sussex, Brighton, UK;Department of Psychology, Peking University, Beijing, China and School of Psychology, University of Birmingham, Birmingham, UK;School of Psychology, University of Birmingham, Birmingham, UK;Laboratory of Neural Information Processing, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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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.