Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination

  • Mathieu J. Ruiz
  • Jean-Michel Hupé
  • Michel Dojat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)


MultiVoxel Pattern Analysis (MVPA) is presented as a successful alternative to the General Linear Model (GLM) for fMRI data analysis. We report different experiments using MVPA to master several key parameters. We found that 1) different feature selections provide similar classification accuracies with different interpretation depending on the underlying hypotheses, 2) paradigms should be created to maximize both Signal to Noise Ratio (SNR) and number of examples and 3) smoothing leads to opposite effects on classification depending on the spatial scale at which information is encoded and should be used with extreme caution.


Machine Learning SVM Neurosciences Brain 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mathieu J. Ruiz
    • 1
    • 2
  • Jean-Michel Hupé
    • 2
  • Michel Dojat
    • 1
  1. 1.GIN - INSERM U836 & Université J. FourierLa TroncheFrance
  2. 2.CerCo - CNRS UMR 5549 & Université de ToulouseToulouseFrance

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