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Decoding Generic Visual Representations from Human Brain Activity Using Machine Learning

  • Angeliki PapadimitriouEmail author
  • Nikolaos Passalis
  • Anastasios Tefas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

Among the most impressive recent applications of neural decoding is the visual representation decoding, where the category of an object that a subject either sees or imagines is inferred by observing his/her brain activity. Even though there is an increasing interest in the aforementioned visual representation decoding task, there is no extensive study of the effect of using different machine learning models on the decoding accuracy. In this paper we provide an extensive evaluation of several machine learning models, along with different similarity metrics, for the aforementioned task, drawing many interesting conclusions. That way, this paper (a) paves the way for developing more advanced and accurate methods and (b) provides an extensive and easily reproducible baseline for the aforementioned decoding task.

Keywords

Neural decoding Deep visual representations 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Artificial Intelligence and Information Analysis LaboratoryAristotle University of ThessalonikiThessalonikiGreece

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