Biological Cybernetics

, Volume 65, Issue 3, pp 203–210 | Cite as

A new method of the description of the information flow in the brain structures

  • M. J. Kaminski
  • K. J. Blinowska


The paper describes the method of determining direction and frequency content of the brain activity flow. The method was formulated in the framework of the AR model. The transfer function matrix was found for multichannel EEG process. Elements of this matrix, properly normalized, appeared to be good estimators of the propagation direction and spectral properties of the investigated signals. Simulation experiments have shown that the estimator proposed by us unequivocally reveals the direction of the signal flow and is able to distinguish between direct and indirect transfer of information. The method was applied to the signals recorded in the brain structures of the experimental animals and also to the human normal and epileptic EEG. The sensitivity of the method and its usefulness in the neurological and clinical applications was demonstrated.


Experimental Animal Transfer Function Clinical Application Brain Activity Brain Structure 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Blinowska KJ, Czerwosz LT, Drabik W, Franaszczuk PJ, Ekiert H (1981) EEG data reduction by means of autoregressive representation and discriminant analysis procedure. Electroencephalogr Clin Neurophysiol 51:650–658Google Scholar
  2. Blinowska KJ, Franaszczuk PJ, Mitraszewski P (1988) A new method of presentation of the average spectral properties of the EEG time series. Int J Biomed Comput 22:97–106Google Scholar
  3. Franaszczuk PJ, Blinowska KJ, Kowalczyk M (1985) The application of parametric multichannel spectral estimates in the study of electrical brain activity. Biol Cybern 51:239–247Google Scholar
  4. Gevins AS (1989) Signs of model making by the human brain. In: Springer Series in Brain Dynamics, vol 4. Basar E, Bullock T (eds). Springer, Berlin Heidelberg New York, pp 408–415Google Scholar
  5. Kamiński M (1988) The EEG signals mutual relationship investigation. MSc Thesis. Warsaw UniversityGoogle Scholar
  6. Kamitake T, Harashima H, Miyakawa H (1984) A time-series analysis method based on the directed transinformation. Electron Commun Jpn 6:103–110Google Scholar
  7. Lehman D, Ozaki H, Pal I (1987) EEG alpha map series: brain microstates by space oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288Google Scholar
  8. Saito Y, Harashima H (1981) Tracking of information within multichannel EEG record-casual analysis in EEG. In: Yamaguchi N, Fujisawa K (eds). Recent advances in EEG and EMG data processing. Elsevier/North-Holland, Amsterdam, pp 133–146Google Scholar
  9. Schnider SM, Kwong RH, Lenz FA, Kwan HC (1989) Detection of feedback in the central nervous system using system identification techniques. Biol Cybern 60:203–212Google Scholar
  10. Thatcher RW, Krause PJ, Hrybyk M (1986) Cortico-cortical associations and EEG coherence: a two-compartamental model. Elec troencephalogr Clin Neurophysiol 64:123–143Google Scholar

Copyright information

© Springer-Verlag 1991

Authors and Affiliations

  • M. J. Kaminski
    • 1
  • K. J. Blinowska
    • 1
  1. 1.Medical Physics Laboratory, Institute of Experimental Physics, Warsaw UniversityWarszawaPoland

Personalised recommendations