title: Adaptive multiclass classification for brain computer interfaces.
abstract: We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten, 2012) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects.
authors.affiliation: Radboud University and Donders Institute for Brain, Cognition and Behaviour, Nijmegen 6525 EZ, Netherlands a.llera@donders.ru.nl.
authors.forename: A
authors.initials: A
authors.lastname: Llera
authors.forename: V
authors.initials: V
authors.lastname: Gómez
authors.forename: H J
authors.initials: HJ
authors.lastname: Kappen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D016208:Databases, Factual; D016002:Discriminant Analysis; D004569:Electroencephalography; D006801:Humans; D012815:Signal Processing, Computer-Assisted