Skip to main content
SpringerLink
Log in

Ensemble Learning Methods for Classifying EEG Signals

  • Conference paper
Multiple Classifier Systems (MCS 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4472))

Included in the following conference series:

Abstract

Bagging, boosting and random subspace are three popular ensemble learning methods, which have already shown effectiveness in many practical classification problems. For electroencephalogram (EEG) signal classification arising in recent brain-computer interface (BCI) research, however, there are almost no reports investigating their feasibilities. This paper systematically evaluates the performance of these three ensemble methods for their new application on EEG signal classification. Experiments are conducted on three BCI subjects with k-nearest neighbor and decision tree as base classifiers. Several valuable conclusions are derived about the feasibility and performance of ensemble methods for classifying EEG signals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Nicolelis, M.A.L.: Actions from Thoughts. Nature 409, 403–407 (2001)

    Article  Google Scholar 

  2. Wolpaw, J.R., et al.: Brain-Computer Interfaces for Communication and Control. Clin. Neurophysiol. 113, 767–791 (2002)

    Article  Google Scholar 

  3. Ebrahimi, T., Vesin, J.M., Garcia, G.: Brain-Computer Interface in Multimedia Communication. IEEE Signal Proc. Mag. 20, 14–24 (2003)

    Article  Google Scholar 

  4. Wolpaw, J.R., et al.: Brain-Computer Interface Technology: a Review of the First International Meeting. IEEE Trans. Rehab. Eng. 8, 164–173 (2000)

    Article  Google Scholar 

  5. Vaughan, T.M.: Guest Editorial Brain-Computer Interface Technology: a Review of the Second International Meeting. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 94–109 (2003)

    Article  Google Scholar 

  6. Garrett, D., et al.: Comparion of Linear, Nonlinear, and Feature Selection Methods for EEG Signal Classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 141–144 (2003)

    Article  Google Scholar 

  7. Müller, K.-R., Anderson, C.W., Birch, G.E.: Linear and Nonlinear Methods for Brain-Computer Interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 165–169 (2003)

    Article  Google Scholar 

  8. Obermaier, B., et al.: Asymmetric Hemisphere Modeling in an Offline Brain-Computer Interface. IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev. 31, 536–540 (2001)

    Article  Google Scholar 

  9. Millán, J.R., et al.: Brain-Actuated Interaction. Artif. Intell. 159, 241–259 (2004)

    Article  Google Scholar 

  10. Sun, S., Zhang, C.: Learning On-Line Classification via Decorrelated LMS Algorithm: Application to Brain-Computer Interfaces. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS (LNAI), vol. 3735, pp. 215–226. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Sun, S., Zhang, C.: Adaptive Feature Extraction for EEG Signal Classification. Medical & Biological Engineering & Computing 44, 931–935 (2006)

    Article  MathSciNet  Google Scholar 

  12. Kamousi, B., Liu, Z., He, B.: Classification of Motor Imagery Tasks for Brain-Computor Interface Applications by Means of Two Equivalent Dipoles Analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 166–171 (2005)

    Article  Google Scholar 

  13. Dietterich, T.G.: Machine Learning Research - Four Current Directions. AI Mag. 18, 97–136 (1997)

    Google Scholar 

  14. Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)

    MathSciNet  MATH  Google Scholar 

  15. Freund, Y., Shapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)

    Article  MATH  Google Scholar 

  16. Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)

    Article  Google Scholar 

  17. Dietterich, T.G.: An Experiemental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Mach. Learn. 40, 139–157 (2000)

    Article  Google Scholar 

  18. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2000)

    Google Scholar 

  19. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  20. Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Diversity in Search Strategies for Ensemble Feature Selection. Information Fusion 6, 83–98 (2005)

    Article  Google Scholar 

  21. Millán, J.R.: On the Need for On-Line Learning in Brain-Computer Interfaces. In: Proc. 2004 Int. Joint Conf. Neural Networks, vol. 4, pp. 2877–2882 (2004)

    Google Scholar 

  22. Chiappa, S., Millán, J.R.: Data set V <mental imagery, multi-class> (2005), Available at http://ida.first.fraunhofer.de/projects/bci/competition_iii/desc_V.html

  23. Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Mach. Learn. 36, 105–139 (1999)

    Article  Google Scholar 

  24. Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  25. Breiman, L.: Heuristics of Instability and Stabilization in Model Selection. Ann. Stat. 24, 2350–2383 (1996)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, East China Normal University, Shanghai, 200062, China

    Shiliang Sun

Authors
  1. Shiliang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Michal Haindl Josef Kittler Fabio Roli

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Sun, S. (2007). Ensemble Learning Methods for Classifying EEG Signals. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72523-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72481-0

  • Online ISBN: 978-3-540-72523-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation