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Brain Topography

, Volume 2, Issue 4, pp 275–284 | Cite as

Spatial patterns underlying population differences in the background EEG

  • Zoltan J. Koles
  • Michael S. Lazar
  • Steven Z. Zhou
Article

Summary

A method is described which can be used to extract common spatial patterns underlying the EEGs from two human populations. These spatial patterns account, in the least-squares sense, maximally for the variance in the EEGs from one population and minimally for the variance in the other population and therefore would seem to be optimal for quantitatively discriminating between the individual EEGs in the two populations. By using this method, it is suggested that the problems associated with the more common approach to discriminating EEGs, significance probability mapping, can be avoided. The method is tested using EEGs from a population of normal subjects and using the EEGs from a population of patients with neurologic disorders. The results in most cases are excellent and the misclassification which occurs in some cases is attributed to the nonhomogeneity of the patient population particularly. The advantages of the method for feature selection, for automatically classifying the clinical EEG, and with respect to the reference-free nature of the selected features are discussed.

Key words

Electroencephalogram Eigenanalysis Spatial patterns Classification 

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References

  1. Duffy, F.H., Bartels, P.H. and Burchfiel, J.L., Significance probability mapping: an aid in the topographic analyses of brain electrical activity. Electroenceph. Clin. Neurophys., 1981, 51: 455–462.Google Scholar
  2. Fukunaga, K., Introduction to Statistical Pattern Recognition, Academic Press, New York, 1972.Google Scholar
  3. Gevins, A.S., Analysis of the electromagnetic signals of the human brain: milestones, obstacles and goals. IEEE Trans. Biomed. Eng., 1984, BME-31: 833–850.Google Scholar
  4. Hjorth, B. and Rodin, E., An eigenfunction approach to the inverse problem of EEG. Brain Topography, 1988, 1: 79–86.Google Scholar
  5. Koles, Z.J., Kasmia, A., Paranjape, R.B. and McLean, D.R., Computed radial-current topography of the brain: patterns associated with the normal and abnormal EEG. Electroenceph. Clin. Neurophys., 1989, 72: 41–47.Google Scholar
  6. Lehmann, D., Principles of spatial analysis. In: A.S. Gevins and A. Remond (Eds.), Handbook of Electroencephalography and Clinical Neurophysiology, Revised Series, Elsevier, Amsterdam, 1987, 1: 309–354.Google Scholar
  7. MATLAB is a trademark of The MathWorks, Inc., 21 Eliot St., South Natick, MA, USA, 01760.Google Scholar
  8. Nuwer, M.R., Quantitative EEG: I. Techniques and problems of frequency analysis and topographic mapping. J. Clin. Neurophys., 1988, 5: 1–43.Google Scholar
  9. Nuwer, M.R., Quantitative EEG: II. Frequency analysis and topographic mapping in clinical settings. J. Clin. Neurophys., 1988, 5: 45–85.Google Scholar
  10. Perrin, F., Bertrand, O. and Pernier, J., Scalp current density mapping: value and estimation from potential data. IEEE Trans. Biomed. Eng., 1987, BME-34: 283–288.Google Scholar

Copyright information

© Human Sciences Press, Inc 1990

Authors and Affiliations

  • Zoltan J. Koles
    • 1
  • Michael S. Lazar
    • 1
  • Steven Z. Zhou
    • 1
  1. 1.Department of Applied Sciences in MedicineUniversity of AlbertaEdmontonCanada

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