On the use of interaction error potentials for adaptive brain computer interfaces

Authors:
A. Llera;M. A. J. van Gerven;V. Gómez;O. Jensen;H. J. Kappen
Affiliations:
Radboud University Nijmegen, The Netherlands and Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands;Radboud University Nijmegen, The Netherlands and Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands and Institute for Computing and Information Sciences (ICIS), Nijmeg ...;Radboud University Nijmegen, The Netherlands and Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands;Radboud University Nijmegen, The Netherlands and Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands;Radboud University Nijmegen, The Netherlands and Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
Venue:
Neural Networks
Year:
2011

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Abstract

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods.