ℓ1-Penalized Linear Mixed-Effects Models for BCI

Abstract

AbstractA 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.KeywordsLinear Discriminant AnalysisBrain Computer InterfaceCommon Spatial PatternBalance DatasetUnbalanced DatasetThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.