PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Brain state differences between calibration and application session influence BCI classification accuracy
Matthias Krauledat, Florian Losch and Gabriel Curio
Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006 pp. 60-61, 2006.

Abstract

The Berlin Brain-Computer Interface (BBCI) has been developed to transfer the main load of learning from the user to the machine. After a short calibration period of approx. 30 minutes, even untrained users with no previous BCI experience can achieve bit-rates of more than 35 bits/min. In some of these experiments, however, the classifier from the calibration period needs to be slightly adapted by adding a constant bias term to its output in order to maintain a stable performance throughout the feedback session. In this paper, we will provide evidence that a change in the brain states between calibration and feedback periods probably causes this need for adaptation.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:2262
Deposited By:Benjamin Blankertz
Deposited On:11 October 2006