PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unsupervised adaptation of the LDA classifier for Brain-Computer Interfaces.
Carmen Vidaurre, Alois Schloegl, Benjamin Blankertz, Motoaki Kawanabe and Klaus-Robert Müller
Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course 2008 pp. 122-127, 2008.

Abstract

This paper discusses simulated on-line unsupervised adaptation of the LDA classier in order to counteract the harmful eect of non-class related non-stationarities in EEG during BCI sessions. Three types of adaptation procedures were applied to the two large BCI data sets from TU Graz and Berlin BCI project. Our results demonstrate that the unsupervised adaptive classiers can improve performance substantially under dierent BCI settings. More importantly, since label information is not necessary, they are applicable to wide ranges of practical BCI tasks.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:5102
Deposited By:Benjamin Blankertz
Deposited On:24 March 2009