title: ℓ(1)-penalized linear mixed-effects models for high dimensional data with application to BCI.
abstract: 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 ℓ(1)-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.
authors.affiliation: Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany; Bernstein Focus: Neurotechnology Berlin (BFNT-B), 10587 Berlin, Germany. fazli@cs.tu-berlin.de
authors.forename: Siamac
authors.initials: S
authors.lastname: Fazli
authors.forename: Márton
authors.initials: M
authors.lastname: Danóczy
authors.forename: Jürg
authors.initials: J
authors.lastname: Schelldorfer
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D001699:Biometry / Q000379:methods*; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D016014:Linear Models; D012815:Signal Processing, Computer-Assisted