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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