1-penalized linear mixed-effects models for BCI

Siamac Fazli, Márton Danóczy, Jürg Schelldorfer, Klaus Robert Müller

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

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings
Number of pages10
EditionPART 1
Publication statusPublished - 2011
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)0302-9743
ISSN (Electronic)1611-3349


Other21st International Conference on Artificial Neural Networks, ICANN 2011

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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