Skip to main content

Adaptive SVM-Based Classification Increases Performance of a MEG-Based Brain-Computer Interface (BCI)

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7552)

Abstract

One problem in current Brain-Computer Interfaces (BCIs) is non-stationarity of the underlying signals. This causes deteriorating performance throughout a session and difficulties to transfer a classifier from one session to another, which results in the need of collecting training data every session. Using an adaptive classifier is one solution to keep the performance stable and reduce the amount of training that is needed for a good BCI performance. In this paper we present an approach for an adaptive classifier based on a Support Vector Machine (SVM). We evaluate its advantage on offline BCI data and show its benefits and online feasibility in an online experiment using a MEG-based BCI with 10 subjects.

Keywords

  • Brain-Computer interface (BCI)
  • adaptive control
  • Support Vector Machine (SVM)

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-33269-2_84
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-33269-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   131.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N., Kübler, A.: An MEG-based brain-computer interface (BCI). NeuroImage 36(3), 581–593 (2007)

    CrossRef  Google Scholar 

  2. Shenoy, P., Krauledat, M., Blankertz, B., Rao, R.P.N., Müller, K.-R.: Towards adaptive classification for BCI (2006)

    Google Scholar 

  3. Blumberg, J., Rickert, J., Waldert, S., Schulze-Bonhage, A., Aertsen, A., Mehring, C.: Adaptive classification for brain computer interfaces. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 2536–2539 (August 2007)

    Google Scholar 

  4. Vidaurre, C., Schlögl, A., Blankertz, B., Kawanabe, M., Müller, K.: Unsupervised adaptation of the lda classifier for brain-computer interfaces. In: Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course, pp. 122–127 (2008)

    Google Scholar 

  5. Liu, G., Zhang, D., Meng, J., Huang, G., Zhu, X.: Unsupervised adaptation of electroencephalogram signal processing based on fuzzy c-means algorithm. International Journal of Adaptive Control and Signal Processing (2011)

    Google Scholar 

  6. Vidaurre, C., Kawanabe, M., von Bünau, P., Blankertz, B., Müller, K.R.: Toward Unsupervised Adaptation of LDA for Brain-Computer Interfaces. IEEE Transactions on Biomedical Engineering 58(3), 587–597 (2011)

    CrossRef  Google Scholar 

  7. Vapnik, V.N.: Statistical Learning Theory, 1st edn. Wiley-Interscience ( September 1998)

    Google Scholar 

  8. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, pp. 92–100. ACM, New York (1998)

    CrossRef  Google Scholar 

  9. Lin, H.-T., Lin, C.-J., Weng, R.: A note on Platt’s probabilistic outputs for support vector machines. Machine Learning 68, 267–276 (2007)

    CrossRef  Google Scholar 

  10. Chen, P.H., Fan, R.E., Lin, C.J.: A study on SMO-type decomposition methods for support vector machines. IEEE Transactions on Neural Networks 17, 893–908 (2006)

    CrossRef  Google Scholar 

  11. Diehl, C.P., Cauwenberghs, G.: SVM Incremental Learning, Adaptation and Optimization. In: Proceedings of the 2003 International Joint Conference on Neural Networks, pp. 2685–2690 (2003)

    Google Scholar 

  12. Bensch, M., Mellinger, J., Bogdan, M., Rosenstiel, W.: A multiclass BCI using MEG. In: Proceedings of the 4th Int. Brain-Computer Interface Workshop, Graz, pp. 191–196 (September 2008)

    Google Scholar 

  13. Spüler, M., Rosenstiel, W., Bogdan, M.: A fast feature selection method for high-dimensional MEG BCI data. In: Proceedings of the 5th Int. Brain-Computer Interface Conference, Graz, pp. 24–27 ( September 2011)

    Google Scholar 

  14. Tamura, H., Kawano, S., Tanno, K.: Unsupervised learning method for a support vector machine and its application to surface electromyogram recognition. Artificial Life and Robotics 14, 362–366 (2009), doi:10.1007/s10015-009-0682-1

    CrossRef  Google Scholar 

  15. Li, Y., Guan, C., Li, H., Chin, Z.: A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system. Pattern Recognition Letters 29(9), 1285–1294 (2008)

    CrossRef  Google Scholar 

  16. Spüler, M., Bensch, M., Kleih, S., Rosenstiel, W., Bogdan, M., Kübler, A.: Online use of error-related potentials in healthy users and people with severe motor impairment increases performance of a P300-BCI. Clinical Neurophysiology 123(7), 1328–1337 (2012)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Spüler, M., Rosenstiel, W., Bogdan, M. (2012). Adaptive SVM-Based Classification Increases Performance of a MEG-Based Brain-Computer Interface (BCI). In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_84

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33269-2_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33268-5

  • Online ISBN: 978-3-642-33269-2

  • eBook Packages: Computer ScienceComputer Science (R0)