PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

ℓ 1-Penalized Linear Mixed-Effects Models for BCI
Siamac Fazli, Marton Danoczy, Jürg Schelldorfer and Klaus-Robert Müller
In: ICANN 2011, 14-17 Jun 2011, Espoo, Finland.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:9480
Deposited By:Benjamin Blankertz
Deposited On:16 March 2012