ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI

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

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ1-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator 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 for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.

Original languageEnglish
Pages (from-to)2100-2108
Number of pages9
Issue number4
Publication statusPublished - 2011 Jun 15


  • BCI
  • Mixed-effects model
  • Sparsity
  • Subject-independent

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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