PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On the use of interaction error potentilas adaptive brain computer interfaces
Alberto Llera, M. van Gerven, Vicenc Gomez, O. Jensen and Bert Kappen
Neural Networks Volume 24, pp. 1120-1127, 2011.

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.

EPrint Type:Article
Additional Information:Error potential Adaptive classification
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:9330
Deposited By:Bert Kappen
Deposited On:16 March 2012