Designing semantic feature spaces for brain-reading

Abstract : We focus on a brain-reading task which consists in discovering a word a person is thinking of based on an fMRI image of their brain. Previous studies have demonstrated the feasibility of this brain-reading task through the design of what has been called a semantic space, i.e. a continuous low dimensional space reflecting the similarity between words. So far the best results have been achieved by carefully designing this semantic space by hand which limits the generalization of such a method. We propose to automatically design several semantic spaces from linguistic resources and to combine them in a principled way and achieve results comparable to that of manually built semantic spaces.
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Contributor : Thierry Artières <>
Submitted on : Wednesday, September 9, 2015 - 4:36:34 PM
Last modification on : Thursday, March 21, 2019 - 1:13:06 PM


  • HAL Id : hal-01196369, version 1


Luepol Pipanmaekaporn, Ludmilla Tajtelbom, Vincent Guigue, Thierry Artières. Designing semantic feature spaces for brain-reading. European Symposium on Artificial Neural Networks, Apr 2015, Bruges, Belgium. pp.433-438. ⟨hal-01196369⟩



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