Recognition of ECoG in BCI systems based on a chaotic neural model

Abstract

For the practical use of brain-computer interface systems, one of the most significant problems is the generalizing ability of the classifiers, since the states of both people and instruments are altering as time goes on. In this paper, a novel chaotic neural network termed KIII model, is introduced to classify single-trial ECoG during motor imagery, acquired in two different sessions. Then, by comparing with other three traditional classifiers, Kill model shows a greater ability to generalize, which demonstrates that KIII model is an effective tool for brain-computer interfaces systems. © Springer-Verlag Berlin Heidelberg 2007.