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Brain Dynamics pp 192-201 | Cite as

A Model of the Generation of Electrocortical Rhythms

  • K. J. Blinowska
  • P. J. Franaszczuk
Part of the Springer Series in Brain Dynamics book series (SSBD, volume 2)

Abstract

The investigation of brain electrical activity is usually approached in one of two ways. In the first approach the methods of analysis of stochastic signals are used to extract the characteristic features of the EEG, without any attempt to elucidate the physiological basis of its generation (Gersh and Yonemoto 1977; Gersh et al. 1977; Bodenstein and Praetorius 1977). In the second approach, models based on the neurophysiological data are created, but very often the results of the modeling are difficult to compare directly with the experimental data and only general features of the EEG are described (Aninos and Zenone 1980; Wilson and Cowan 1973; Zetterberg 1973).

Keywords

Impulse Response Function Neural Population Average Power Spectrum Motor Sensory Cortex Basic Rhythm 
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. Aninos PA, Zenone S (1980) A neural net model for the α-rhythm. Biol Cybern 36: 187–191CrossRefGoogle Scholar
  2. Başar E (1983) Toward a physical approach to integrative physiology. I: Brain dynamics and physiological causality. Am J Physiol 245: R510–R533PubMedGoogle Scholar
  3. Blinowska KJ, Kowalczyk M, Franaszczuk PJ, Mitraszewski P (1988) The application of a new method of parametrization of EEG time series in the study of nociception. Acta Neurobiol Exp (in press)Google Scholar
  4. Bodenstein G, Praetorius HM (1977) Feature extraction from the electroencephalogram by adaptive segmentation. Proc IEEE 65: 642–657CrossRefGoogle Scholar
  5. Franaszczuk PJ, Blinowska KJ (1985) Linear model of brain electrical activity—EEG as a superposition of damped oscillatory modes. Biol Cybern 53: 19–25PubMedCrossRefGoogle Scholar
  6. Franaszczuk PJ, Blinowska KJ, Kowalczyk M (1985) The application of parametric multichannel spectral estimates in the study of electrical brain activity. Biol Cybern 51: 239–247PubMedCrossRefGoogle Scholar
  7. Freeman WJ (1975) Mass action in the nervous system. Academic Press, New YorkGoogle Scholar
  8. Gersh W, Yonemoto J (1977) Parametric time series models for multivariate EEG analysis. Comput Biomed Res 10: 113–125CrossRefGoogle Scholar
  9. Gersh W, Yonemoto J, Naitoh P (1977) Automatic classification measure and the eigenvalues of parametric time series model feature. Comput Biomed Res 10: 297–318CrossRefGoogle Scholar
  10. Isaksson A, Wennberg A, Zetterberg LH (1981) Computer analysis of EEG signals with parametric models. Proc IEEE 69: 451–461CrossRefGoogle Scholar
  11. Lopes da Silva FH, Van Rotterdam A, Barts P, Van Heusden E, Burr W (1976) Models of neuronal populations: the basic mechanisms of rhythmicity. In: Corner MA, Scrab OF (eds) Perspectives of brain research, pp 281–308 (Progress in brain research, vol 45 )CrossRefGoogle Scholar
  12. Mitraszewski P, Blinowska KJ, Franaszczuk PJ, Kowalczyk M (1987) A study of stability of electrocortical rhythm generators. Biol Cybern 56: 255–260PubMedCrossRefGoogle Scholar
  13. Rabiner LR, Gold B (1975) Theory and application of digital signal processing. Prentice Hall, Englewood CliffsGoogle Scholar
  14. Wilson HR, Cowan JD (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13: 15–80CrossRefGoogle Scholar
  15. Wright JJ, Kydd RR (1984) A linear theory for global electrocortical activity and its control by the lateral hypothalamus. Biol Cybern 50: 75–82PubMedCrossRefGoogle Scholar
  16. Wright JJ, Kydd RR, Lees GJ (1985) State changes in the brain viewed as linear steady states and non-linear transitions between steady states. Biol Cybern 53: 11–17PubMedCrossRefGoogle Scholar
  17. Zetterberg LH (1973) Experience with analysis and simulation of EEG signals with parametric description of spectra. In: Kellaway P, Petersen I (eds) Automation of clinical electroencephalography. Raven, New York, pp 227–234Google Scholar
  18. Zetterberg LH, Kristiansson L, Mossberg K (1978) Performance of a model for a local neuron population. Biol Cybern 31: 15–28PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • K. J. Blinowska
  • P. J. Franaszczuk

There are no affiliations available

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