Machine Vision and Applications

, Volume 24, Issue 6, pp 1311–1325 | Cite as

Mind reading with regularized multinomial logistic regression

  • Heikki Huttunen
  • Tapio Manninen
  • Jukka-Pekka Kauppi
  • Jussi Tohka
Original Paper


In this paper, we consider the problem of multinomial classification of magnetoencephalography (MEG) data. The proposed method participated in the MEG mind reading competition of ICANN’11 conference, where the goal was to train a classifier for predicting the movie the test person was shown. Our approach was the best among ten submissions, reaching accuracy of 68 % of correct classifications in this five category problem. The method is based on a regularized logistic regression model, whose efficient feature selection is critical for cases with more measurements than samples. Moreover, a special attention is paid to the estimation of the generalization error in order to avoid overfitting to the training data. Here, in addition to describing our competition entry in detail, we report selected additional experiments, which question the usefulness of complex feature extraction procedures and the basic frequency decomposition of MEG signal for this application.


Logistic regression Elastic net regularization Classification Decoding Magnetoencephalography Natural stimulus 



The research was funded by the Academy of Finland grant no 130275. We also want to thank Professor R. Hari (Brain Research Unit, Low Temperature Laboratory, Aalto University School of Science, Finland) for her valuable remarks concerning our study.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Heikki Huttunen
    • 1
  • Tapio Manninen
    • 1
  • Jukka-Pekka Kauppi
    • 2
  • Jussi Tohka
    • 1
  1. 1.Department of Signal ProcessingTampere University of TechnologyTampereFinland
  2. 2.Department of Computer Science and HIITUniversity of HelsinkiHelsinkiFinland

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