PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A bayesian approach for adaptive BCI classification
Motoaki Kawanabe, Matthias Krauledat and Benjamin Blankertz
Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006 pp. 54-55, 2006.

Abstract

In this article, we present an adaptive classifier for BCI based on a mixture of Gaussian (moG) model of the features and a dynamical Bayesian model of the class means. We apply this approach to feedback data from the Berlin Brain-Computer Interface (BBCI). The proposed model can improve the classification performance by compensating for substantial changes of EEG signals between training and feedback sessions as well as for gradual nonstationarity in the feedback sessions.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:2260
Deposited By:Benjamin Blankertz
Deposited On:27 October 2006