Brain Computer Interfaces: A Recurrent Neural Network Approach

  • Gareth Oliver
  • Tom Gedeon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6444)

Abstract

This paper explores the use of recurrent neural networks in the field of Brain Computer Interfaces(BCI). In particular it looks at a recurrent neural network, an echostate network and a CasPer neural network and attempts to use them to classify data from BCI competition III’s dataset IVa. In addition it proposes a new method, EchoCasPer, which uses the CasPer training scheme in a recurrent neural network. The results showed that temporal information existed within the BCI data to be made use of, but further pre-processing and parameter exploration was needed to reach competitive classification rates.

Keywords

BCI RNN CasPer Echostate Network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Werbos, P.J.: Backpropagation through time: What it does and how to do it. Proceedings of IEEE 78, 1550–1560 (1990)CrossRefGoogle Scholar
  2. 2.
    Ozturk, M.C., Xu, D., Principe, J.C.: Analysis and design of echostate networks. Neural Computation 19, 111–138 (2006)CrossRefGoogle Scholar
  3. 3.
    Mezzano, T.: Echo State Networks application on maze problems. PhD thesis, Katholieke Universiteit Leuven (2007)Google Scholar
  4. 4.
    Treadgold, N.K., Gedeon, T.D.: A cascade network employing progressive rprop. In: International Work Conference on Artificial and Natural Neural Networks, pp. 733–742 (1997b)Google Scholar
  5. 5.
    Treadgold, N.K., Gedeon, T.D.: Extending and Benchmarking the CasPer Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence (1997a)Google Scholar
  6. 6.
    Jaeger, H.: Short term memory in echostate networks. Technical report (2002)Google Scholar
  7. 7.
    Blankertz, B., Muller, K.R., Krusienski, D., Schalk, G., Wolpaw, J.R., Schlogl, A., Pfurtscheller, G., Millan, J.R., Schroder, M., Birbaumer, N.: The bci competition iii: Validating alternative approachs to actual bci problems. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14, 153–159 (2006)CrossRefGoogle Scholar
  8. 8.
    Wang, Y., Zhang, Z., Li, Y., Gao, X., Gao, S., Yang, F.: Bci competition 2003data set iv: An algorithm based on cssd and fda for classifying single-trial eeg. IEEE Transactions on Biomedical Engineering 51, 1081–1086 (2004)CrossRefGoogle Scholar
  9. 9.
    Zhu, X., Guan, C., Wu, J., Cheng, Y., Wang, Y.: Expectation maximization method for eeg-based continuous cursor control. EURASIP Journal on Advances in Signal Processing (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gareth Oliver
    • 1
  • Tom Gedeon
    • 1
  1. 1.Australian National UniversityAustralia

Personalised recommendations