Searchlight based feature extraction

Shahar Jamshy*, Omri Perez, Yehezkel Yeshurun, Talma Hendler, Nathan Intrator

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


A multi voxel pattern analysis classification framework suitable for neuroimaging data is introduced. The framework includes a novel feature extraction method that uses local modeling based on domain specific knowledge, and therefore, can produce better whole-brain global classification performance using a smaller number of features. In particular, the method includes spherical searchlights in combination with local SVM modeling. The performance of the framework is demonstrated on a challenging fMRI classification problem, and is found to be superior to the performance of state-of-the-art feature selection methods used in neuroimaging.

Original languageEnglish
Pages (from-to)17-25
Number of pages9
JournalLecture Notes in Computer Science
Volume7263 LNAI
StatePublished - 2012
EventInternational Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011 - Sierra Nevada, Spain
Duration: 16 Dec 201117 Dec 2011


  • MVPA
  • SVM
  • Searchlight
  • fMRI


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