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Modeling Spatiotemporal Structure in fMRI Brain Decoding Using Generalized Sparse Classifiers
 

Summary: Modeling Spatiotemporal Structure in fMRI Brain
Decoding Using Generalized Sparse Classifiers
Bernard Ng
Biomedical Signal and Image Computing Lab
The University of British Columbia
Vancouver, Canada
bernardyng@gmail.com
Rafeef Abugharbieh
Biomedical Signal and Image Computing Lab
The University of British Columbia
Vancouver, Canada
rafeef@ece.ubc.ca
Abstract--The curse of dimensionality constitutes a major
challenge to functional magnetic resonance imaging (fMRI)
classification. Coupled with the typically strong noise in fMRI
data, prediction accuracy is often limited. In this paper, we
propose exploiting the inherent spatiotemporal structure of brain
activity to regularize the typically ill-conditioned fMRI
classification problem. To impose a spatiotemporal prior, we
employ a recent classifier learning formulation for building

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences