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ICANN ’93 pp 882-887 | Cite as

Using Selforganizing Feature Maps to Classify EEG Coherence Maps

  • Georg Dorffner
  • Peter Rappelsberger
  • Arthur Flexer
Conference paper

Abstract

In this work we have been applying self-organizing feature maps [3] to the problem of unsupervised classification of EEG data. The type of EEG used are so-called coherence maps based on 19 electrodes, which were derived during specific cognitive taks such as mental rotation. The goal was to exploit the network learning scheme as extractor for any task- (or other parameter-) related information in the data. In other words, we used the self-organizing feature maps to detect whether the EEG inputs can be classified acording to underlying parameters such as the type of task performed. This paper reports about the very promising results of the experiments.

Keywords

Mental Rotation Unsupervised Classification Cognitive Parameter Frequency Band Beta1 Interhemispheric Coherence 
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]
    Amthauer R.: IST 70 — Intelligenz-Struktur-Test, Verlag Hogrefe, 1970.Google Scholar
  2. [2]
    Elo P., J. Saarinen, A. Värri, H. Nieminen, K. Kaski: Classification of Epileptic EEG by Using Self-Organizing Maps, in Aleksander I., Taylor J. (eds.): Artificial Neural Networks 2, Elsevier Science Publishers, 1147–1150, 1992.Google Scholar
  3. [3]
    Kohonen T.: Self-organized formation of topologically correct feature maps, Biological Cybernetics 43:59–69, 1982.MathSciNetCrossRefMATHGoogle Scholar
  4. [4]
    Litscher G., Flotzinger D., Pfurtscheller G.: Die Verwendung neuronaler Netzwerke bei der Mustererkennung von Schlafprofilen; to appear in: Löffler (ed.): Central Nervous System Monitoring, Verlag Wilhelm Maudrich Wien-München-Bern, 1993.Google Scholar
  5. [5]
    Lo P.-C., Principe J.C.: Dimensionality Analysis of EEG Segments: Experimental Considerations, in IEEE International Conference On Neural Networks, Washington D.C., IEEE, Volume 1, pp.693–698, 1989.Google Scholar
  6. [6]
    Rappelsberger P., Dorffner G., Flexer A.: Classification of EEG coherence maps of cognitive processes; to appear in: Löffler (ed.): Central Nervous System Monitoring, Verlag Wilhelm Maudrich Wien-München-Bern, 1993.Google Scholar
  7. [7]
    Ritter H., Martinetz T., Schulten K.: Neuronale Netze, Addison-Wesley, Reading, MA, 1990.Google Scholar
  8. [8]
    Shepard R.N., Cooper L.A.: Mental images and their transformations, MIT Press Cambridge, 1982.Google Scholar
  9. [9]
    Stumpf H., Fay E.: Schlauchfiguren: Ein Test zur Beurteilung des räumlichen Vorstellungsvermögens, 1983.Google Scholar
  10. [10]
    Wang G., Takigawa M., Miyazaki T., Takeishi T.: Analysis of EEG Changes Between Frontal and Occipital Area in Speaking Process, in Caudill M. (ed.), Proceedings of the International Joint Conference on Neural Networks (Winter Meeting), Washington D.C., Lawrence Erlbaum, Hillsdale, NJ, pp.27–30, 1990.Google Scholar

Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Georg Dorffner
    • 1
  • Peter Rappelsberger
    • 2
  • Arthur Flexer
    • 1
  1. 1.Dept. of Medical Cybernetics and Artificial IntelligenceUniversity of Vienna, and Austrian Research Institute for Artificial IntelligenceAustria
  2. 2.Inst. of NeurophysiologyUniversity of ViennaAustria

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