Skip to main content

EEG-based cognitive task classification with ICA and neural networks

  • Bio-inspired Systems
  • Conference paper
  • First Online:
Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

Included in the following conference series:

  • 185 Accesses

Abstract

Electroencephalography (EEG) has been used extensively for classifying cognitive tasks. Many investigators have demonstrated classification accuracies well over 90% for some combinations of cognitive tasks, signal transformations, and classification methods. Unfortunately, EEG data is prone to significant interference from a wide variety of artifacts, particularly eye blinks. Most methods for classifying cognitive tasks with EEG data simply discard time windows containing eye blink artifacts. However, future applications of EEG-based cognitive task classification should not be hindered by eye blinks. The value of an EEG-controlled human-computer interface, for instance, would be severely diluted if it did not work in the presence of eye blinks. Fortunately, recent advances in blind signal separation algorithms and their applications to EEG data mitigate the artifact contamination issue. In this paper, we show how independent components analysis (ICA) and its extension for sub-Gaussian sources, extended ICA (eICA), can be applied to accurately classify cognitive tasks with eye blink contaminated EEG recordings.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. W. Anderson, E. A. Stolz, and S. Shamsunder. Multivariate autoregressive models for classification of spontaneous electroencephalogram during mental tasks. IEEE Transactions on Biomedical Engineering, 45(3):277–286, 1998.

    Article  Google Scholar 

  2. Charles W. Anderson. Effects of variations in neural network topology and output averaging on the discrimination of mental tasks from spontaneous electroencephalogram. Journal of Intelligent Systems, 7(1–2):165–190, 1997.

    Google Scholar 

  3. A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6):1129–1159, November 1995.

    Google Scholar 

  4. M. T. Hagan and M. Menjah. Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neural Networks, 5(6):989–993, 1994.

    Article  Google Scholar 

  5. Tzyy-Ping Jung, Colin Humphries, Te Won Lee, Scott Makeig, Martin J. McKeown, Vicente Iragui, and Terrence J. Sejnowski. Extended ica removes artifacts from electroencephalographic recordings. In to appear, editor, Advances in Neural Information Processing Systems 10. The MIT Press, Cambridge, MA, 1998.

    Google Scholar 

  6. Z. A. Keirn. Alternative modes of communication between man and machine. Master's thesis, Purdue University, Lafayette, IN, West Lafayette, IN, 1988.

    Google Scholar 

  7. Scott Makeig, Anthony J. Bell, Tzyy-Ping Jung, and Terrence J. Sejnowski. Independent component analysis of electroencephalographic data. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 8, pages 145–151. The MIT Press, Cambridge, MA, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

José Mira Juan V. Sánchez-Andrés

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Peterson, D.A., Anderson, C.W. (1999). EEG-based cognitive task classification with ICA and neural networks. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100493

Download citation

  • DOI: https://doi.org/10.1007/BFb0100493

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics