1-Penalized Linear Mixed-Effects Models for BCI

  • Siamac Fazli
  • Márton Danóczy
  • Jürg Schelldorfer
  • Klaus-Robert Müller
Conference paper

DOI: 10.1007/978-3-642-21735-7_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6791)
Cite this paper as:
Fazli S., Danóczy M., Schelldorfer J., Müller KR. (2011) ℓ1-Penalized Linear Mixed-Effects Models for BCI. In: Honkela T., Duch W., Girolami M., Kaski S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg

Abstract

A recently proposed novel statistical model estimates population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We apply this ℓ1-penalized linear regression mixed-effects model to a large scale real world problem: by exploiting a large set of brain computer interface data we are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now able to differentiate within-subject and between-subject variability. A deeper understanding both of the underlying statistical and physiological structure of the data is gained.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Siamac Fazli
    • 1
  • Márton Danóczy
    • 1
  • Jürg Schelldorfer
    • 2
  • Klaus-Robert Müller
    • 1
  1. 1.Berlin Institute of TechnologyBerlinGermany
  2. 2.ETH ZürichZürichSwitzerland

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