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EEG Signal Classification Using Neural Networks

  • George M. Papadourakis
  • Sifis Micheloyannis
  • George Bebis
  • Manolis Giachnakis
Chapter
Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)

Abstract

The application of Artificial Neural Networks (ANN) to electroencephalographic (EEG) signal classification is presented. Initially, the power spectrum and coherence “reactivity” parameters are extracted from the EEG signals in order to provide the inputs to the ANNs. In addition, traditional statistical and classification methods are utilized to improve the accuracy of the ANN classifiers. Various ANN experiments are performed and their results are discussed.

Keywords

Power Spectrum Artificial Neural Network Classifier Parallel Distribute Process Total Power Spectrum ANNs Ability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    W. Freeman Analytic Techniques Used in the Search for the Physiological Basis of the EEG, Methods of Analysis of Brain Electrical and Magnetic Signals. EEG Handbook (revised series Vol. 1), A.S. Gevins and A. Remond (Eds), 1987.Google Scholar
  2. [2]
    G. Dumermuth and L. Molinari Spectral Analysis of the EEG, Neuropsychobiology, Vol. 17, 1987.Google Scholar
  3. [3]
    M. Nuwer Quantitative EEG I + II, Journal of Clinical Neurophysiology, Vol. 5, 1988.Google Scholar
  4. [4]
    S. Metric and R. Brenner, Abnormal brainstem auditory evoked potentials in chronic paint sniffers, Annals of Neurology, Vol. 16, 1982.Google Scholar
  5. [5]
    A. Seppaiainen, Neurophysiological findings among workers exposed to organic solvents, Scandinavian Journal of Work and Environmental Health, Vol. 7, 1981.Google Scholar
  6. [6]
    A. Seppaiainen, Neurophysiological aspects of the toxisity of organic solvents, Scandinavian Journal of Work and Environmental Health, Vol. 11, 1985.Google Scholar
  7. [7]
    S. Micheloyannis, N. Paritsis and P. Trikas, EEG Coherence during hemispheric activation in schizophrenics, European Archives of Psychiatry and Clinical Neuroscience, in press.Google Scholar
  8. [8]
    W. Mendenhall Introduction to Probability and Statistics, Duxbury Press, 1979.Google Scholar
  9. [9]
    G. Bebis, G. Papadourakis and M. Georgiopoulos Back Propagation: Increasing Rate of Convergence by Predictable Pattern Loading, Intelligent Systems Review, vol. 1, No 3, 1989.Google Scholar
  10. [10]
    G. Dumetmuth and L. Molinari, Spectral analysis of EEG Background activity, EEG Handbook: Methods of Analysis of Brain Electrical and Magnetic Signals, Elsevier Science Publishers, 1987.Google Scholar
  11. [11]
    D. Touretzky and D. Pomerleau What’s Hidden in the Hidden Layers ?, Byte Magazine, August 1989.Google Scholar
  12. [12]
    D. E. Rumelhart, J. L. McClelland, and the PDP Research Group. “Parallel Distributed Processing (PDP), Explorations in the Microstructure of Cognition, Volume 1: Foundations. MIT Press, Cambridge, Massachusetts, 1986.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • George M. Papadourakis
    • 1
  • Sifis Micheloyannis
    • 2
  • George Bebis
    • 1
  • Manolis Giachnakis
    • 2
  1. 1.Institute of Computer ScienceFoundation of Research and TechnologyIraklion, CreteGreece
  2. 2.Department of MedicineUniversity of CreteIraklion, CreteGreece

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