1-penalized linear mixed-effects models for BCI

Siamac Fazli, Márton Danóczy, Jürg Schelldorfer, Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

A recently proposed novel statistical model estimates population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We apply this ℓ 1-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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages26-35
Number of pages10
Volume6791 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2011 Jun 24
Externally publishedYes
Event21st International Conference on Artificial Neural Networks, ICANN 2011 - Espoo, Finland
Duration: 2011 Jun 142011 Jun 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6791 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other21st International Conference on Artificial Neural Networks, ICANN 2011
CountryFinland
CityEspoo
Period11/6/1411/6/17

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Fazli, S., Danóczy, M., Schelldorfer, J., & Muller, K. (2011). 1-penalized linear mixed-effects models for BCI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6791 LNCS, pp. 26-35). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6791 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21735-7_4