Network Boosting for BCI Applications

  • Shijun Wang
  • Zhonglin Lin
  • Changshui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wolpaw, J., et al.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Wang, S.J., Zhang, C.S.: Network game and boosting. In: The 16th European Conference on Machine Learning (2005)Google Scholar
  5. 5.
    Wang, S.J., Zhang, C.S.: Weighted competition scale-free network. Phys. Rev. E 70, 066127 (2004)CrossRefGoogle Scholar
  6. 6.
    Albert, R., Barabási, A.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002)CrossRefMathSciNetzbMATHGoogle Scholar
  7. 7.
  8. 8.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shijun Wang
    • 1
  • Zhonglin Lin
    • 1
  • Changshui Zhang
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

Personalised recommendations