PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

L1-penalized Linear Mixed-Effects Models for high dimensional data with application to BCI
Siamac Fazli, Marton Danoczy, Juerg Schelldorfer and Klaus-Robert Müller
NeuroImage Volume 56, Number 4, pp. 2100-2108, 2011. ISSN 1053-8119

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 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:9478
Deposited By:Benjamin Blankertz
Deposited On:16 March 2012