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, KIII model shows a greater ability to generalize, which demonstrates that KIII model is an effective tool for brain-computer interfaces systems.
This work is supported in part by National Natural Science Foundation of China Grant #60421002 and the National Basic Research Program of China (973 Program) Grant #2004CB720302. The authors would like to thank Wolfgang Rosenstiel, Niels Birbaumer, Bernhard Schölkopf and Christian Elger for providing the dataset.
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Hu, R., Li, G., Hu, M., Fu, J., Freeman, W.J. (2007). Recognition of ECoG in BCI Systems Based on a Chaotic Neural Model. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_81
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DOI: https://doi.org/10.1007/978-3-540-72383-7_81
Publisher Name: Springer, Berlin, Heidelberg
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