Abstract
Network Boosting is an ensemble learning method which combines learners together based on a network and can learn the target hypothesis asymptotically. We apply the approach to analyze data from the P300 speller paradigm. The result on the Data set II of BCI (Brain-computer interface) competition III shows that Network Boosting achieves higher classification accuracy than logistic regression, SVM, Bagging and AdaBoost.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wolpaw, J., et al.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prothesis utilizing event-related brain potentials. Electroenceph. Clin. Neurophysiol. 70, 510–523 (1988)
Donchin, E., et al.: The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehabi. Eng. 8, 174–179 (2000)
Wang, S.J., Zhang, C.S.: Network game and boosting. In: The 16th European Conference on Machine Learning (2005)
Wang, S.J., Zhang, C.S.: Weighted competition scale-free network. Phys. Rev. E 70, 066127 (2004)
Albert, R., Barabási, A.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002)
http://ida.first.fraunhofer.de/projects/bci/competition_iii/
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, S., Lin, Z., Zhang, C. (2005). Network Boosting for BCI Applications. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds) Discovery Science. DS 2005. Lecture Notes in Computer Science(), vol 3735. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563983_38
Download citation
DOI: https://doi.org/10.1007/11563983_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29230-2
Online ISBN: 978-3-540-31698-5
eBook Packages: Computer ScienceComputer Science (R0)