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Biological Cybernetics

, Volume 106, Issue 6–7, pp 389–405 | Cite as

Regularized logistic regression and multiobjective variable selection for classifying MEG data

  • Roberto Santana
  • Concha Bielza
  • Pedro Larrañaga
Original Paper

Abstract

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Keywords

Brain computer interface MEG Multiobjective optimization Classification Feature subset selection Probabilistic modeling 

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Roberto Santana
    • 1
  • Concha Bielza
    • 2
  • Pedro Larrañaga
    • 2
  1. 1.Intelligent Systems GroupUniversity of the Basque Country (UPV/EHU)San SebastianSpain
  2. 2.Computational Intelligence GroupDepartamento de Inteligencia Artificial Universidad Politécnica de MadridBoadilla del Monte, MadridSpain

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