Multi-Voxel Pattern Analysis of fMRI Based on Deep Learning Methods

  • Yutaka Hatakeyama
  • Shinichi Yoshida
  • Hiromi Kataoka
  • Yoshiyasu Okuhara
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 271)


A decoding process for fMRI data is constructed based on Multi-Voxel Pattern Analysis (MVPA) using deep learning method for online training process. The constructed process with Deep Brief Network (DBN) extracts the feature for classification on each ROI of input fMRI data. The decoding experiment results for hand motion show that the decoding accuracy based on DBN is comparable to that with the conventional process with batch training and that the divided feature extraction in the first layer decreases computational time without loss of accuracy. The constructed process should be necessary for interactive decoding experiments for each subject.


fMRI MVPA deep learning Deep Brief Network 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yutaka Hatakeyama
    • 1
  • Shinichi Yoshida
    • 2
  • Hiromi Kataoka
    • 1
  • Yoshiyasu Okuhara
    • 1
  1. 1.Center of Medical Information ScienceKochi UniversityKochiJapan
  2. 2.School of InformationKochi University of TechnologyKochiJapan

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