We should not get stuck in purely theoretical considerations concerning synchronization and desynchronization processes. Rather we should profit from the theory and use it as a basis for designing and evaluating phase resetting experiments. Consequently, this chapter is devoted to the data analysis tools throwing a bridge across the gap between modelling and experiment.


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  1. Ahonen, A.I., Hämäläinen, M.S., Kajola, M.J., Knuutila, J.E.T., Lounasmaa, O.V., Simola, J.T., Tesche, C.D., Vilkman, V.A. (1991): Multichannel SQUID systems for brain research, IEEE Trans. Magn. 27, 2786–2792ADSCrossRefGoogle Scholar
  2. Andrä, W., Nowak, H. (eds.) (1998): Magnetism in Medicine, Wiley-VCH, BerlinGoogle Scholar
  3. Basar, E. (1998a): Brain Oscillations, Springer, Berlin; (1998b): Integrative Brain Function, Springer, BerlinGoogle Scholar
  4. Berger, H. (1929): Uber das Elektroenkephalogramm des Menschen, Arch. Psychiatr. Nervenkr. 87, 527–570Google Scholar
  5. Clarke, C.J.S., Janday, B.S. (1989): The solution of the biomagnetic inverse problem by maximum statistical entropy, Inverse Problems 95, 483–500MathSciNetADSCrossRefGoogle Scholar
  6. Clarke, C.J.S., loannides, A.A., Bolton, J.P.R. (1990): Localised and distributed source solutions for the biomagnetic inverse problem I. In: Advances in Bio-magnetism, Williamson, S.J., Hoke, M., Stroink, G., Kotani, M. (eds.), Plenum Press, New York, 587–590Google Scholar
  7. Cohen, D. (1972): Magnetoencephalography: Detection of the brain’s electrical activity with a superconducting magnetometer, Science 175, 664–666ADSCrossRefGoogle Scholar
  8. Cooper, R., Osselton, J.W., Shaw, J.C. (1984): Elektroenzephalographie: Technik and Methoden, 3rd. rev. ed., Gustav Fischer Verlag, StuttgartGoogle Scholar
  9. Creutzfeldt, O.D. (1983): Cortex Cerebri, Springer, BerlinCrossRefGoogle Scholar
  10. Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M., Reitboeck, H.J. (1988): Coherent oscillations: a mechanism of feature linking in the visual cortex?, Biol. Cybern. 60, 121–130CrossRefGoogle Scholar
  11. Eckhorn, R., Dicke, P., Kruse, W., Reitboeck, H.-J. (1990): Stimulus-related facilitation and synchronization among visual cortical areas. In: Nonlinear Dynamics and Neural Networks, Schuster, H.G., Singer, W. (eds.), VCH, WeinheimGoogle Scholar
  12. Elble, R.J., Koller, W.C. (1990): Tremor, John Hopkins University Press, BaltimoreGoogle Scholar
  13. Feldman, M.S. (1985): Investigation of the natural vibrations of machine elements using Hilbert transform, Sov. Machine Sci. 2, 44–47; (1994): Nonlinear systemGoogle Scholar
  14. vibration analysis using Hilbert transform. I. Free vibration analysis method “FREEVIB”, Mech. Systems and Signal Processing 8, 119–127Google Scholar
  15. Feldman, M.S., Rosenblum, M.G. (1988): Computer program for determination of nonlinear elastic and damping properties of a vibrating system. In: Proceedings of the Workshop “Software in Machine Building CAD Systems”, 89. Izhevsk (In Russian)Google Scholar
  16. Freeman, W.J. (1975): Mass action in the nervous system: Examination of the neurophysiological basis of adaptive behavior through the EEG, Academic Press, LondonGoogle Scholar
  17. Freund, H.-J. (1983): Motor unit and muscle activity in voluntary motor control, Physiological Reviews 63, 387–436Google Scholar
  18. Friedrich, R., Fuchs, A., Haken, H. (1992): Spatio-temporal EEG-patterns. In: Rhythms in biological systems, Haken, H., Köpchen, H.P. (eds.), Springer, BerlinGoogle Scholar
  19. Friedrich, R., Uhl, C. (1992): Synergetic analysis of human electroencephalograms: Petit-mal epilepsy. In: Evolution of dynamical structures in complex systems, Friedrich, R., Wunderlin, A. (eds.), Springer, BerlinCrossRefGoogle Scholar
  20. Fuchs, A., Friedrich, R., Haken, H., Lehmann, D. (1987): Spatio-temporal analysis of multichannel a-EEG map series. In: Computational systems - natural and artificial, Haken, H. (ed.), Springer, BerlinGoogle Scholar
  21. Fuchs, M., Wagner, M., Wischmann H.A., Dössel, O. (1995): Cortical current imaging by morphologically constrained reconstructions. In: Biomagnetism: Fundamental Research and Clinical Applications, Baumgartner, C., Deeke, L., Stroink, G., Williamson, S.J. (eds.), Elsevier Science Publishers, Amsterdam, 299–301Google Scholar
  22. Glass, L., Mackey, M.C. (1988): From Clocks to Chaos. The Rhythms of Life,Princeton University PressGoogle Scholar
  23. Gray, C.M., Singer, W. (1987): Stimulus specific neuronal oscillations in the cat visual cortex: a cortical function unit, Soc. Neurosci. 404, 3Google Scholar
  24. Gray, C.M., Singer, W. (1989): Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex, Proc. Natl. Acad. Sci. USA 86, 1698–1702ADSCrossRefGoogle Scholar
  25. Greenblatt, R.E. (1993): Probabilistic reconstruction of multiple sources in the bioelectromagnetic inverse problem, Inverse Problems 9, 271–284ADSzbMATHCrossRefGoogle Scholar
  26. Haken, H. (1977): Synergetics. An Introduction, Springer, Berlin; (1983): Advanced Synergetics, Springer, BerlinGoogle Scholar
  27. Haken, H., Koepchen, H.P. (eds.) (1991): Rhythms in Physiological Systems, Springer, BerlinGoogle Scholar
  28. Hämäläinen, M., Hari, R., Ilmoniemi, R.J.,Knuutila, J., Lounasmaa, O.V. (1993): Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain, Rev. Mod. Phys. 65, 413–497Google Scholar
  29. Hari, R., Salmelin, R. (1997): Human cortical oscillations: a neuromagnetic view through the skull, TINS 20, 44–49Google Scholar
  30. Haykin, S. (1986): Adaptive Filter Theory, Prentice Hall, Englewood Cliffs, New JerseyGoogle Scholar
  31. Hefter, H., Logogian, E., Witte, O.W., Reiners, K., Freund, H.-J. (1992): Oscillatory activity in different motor subsystems in palatal myoclonus. A case report, Acta Neurol. Scand. 86, 176–183Google Scholar
  32. Helmholtz, H., von (1853): Uber einige Gesetze der Verteilung elektrischer Ströme in körperlichen Leitern, mit Anwendung auf die tierisch-elektrischen Versuche, Ann. Phys. Chem. 89, 211–233, 353–377ADSGoogle Scholar
  33. Hildebrandt, G. (1982): The time structure of autonomous processes, In: Biological Adaptation, Hildebrandt, G., Hensel, H. (eds.), Georg Thieme, Stuttgart (1987): The autonomous time structure and its reactive modifications in the human organism, In: Temporal Disorder in Human Oscillatory Systems, Rensing, L., an der Heiden, U., Mackey, M.C. (eds.), Springer, BerlinGoogle Scholar
  34. Ioannides, A.A., Bolton, J.P.R., Clarke, C.J.S. (1990): Continuous probabilistic solutions to the biomagnetic inverse problem, Inverse Problems 6, 523–542ADSzbMATHCrossRefGoogle Scholar
  35. Jansen, B.H., Brandt, M.E. (1993): Nonlinear dynamical analysis of the EEG, World Scientific, SingaporeGoogle Scholar
  36. Müller, M.M., Junghöfer, M., Elbert, T., Rochstroh, B. (1997): Visually induced gamma-band responses to coherent and incoherent motion: a replication study, NeuroReport 8, 2575 2579Google Scholar
  37. Niedermeyer, E., Lopes da Silva, F. (1987): Electroencephalography - Basic Principles, Clinical Applications and related Fields, 2nd ed., Urban Si Schwarzenberg, BaltimoreGoogle Scholar
  38. Nunez, P.L. (1995): Neocortical dynamics and human EEG rhythms, Oxford University Press, OxfordGoogle Scholar
  39. Ogawa, S., Lee, T.M., Nayak, A.S., Glynn, P. (1990): Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields, Magnetic Resonance in Medicine 14, 68–78CrossRefGoogle Scholar
  40. Otnes, R.K., Enochson, L. (1972:) Digital Time Series Analysis John Wiley and Sons, New YorkGoogle Scholar
  41. Panter, P. (1965): Modulation, Noise, and Spectral Analysis, McGraw-Hill, New YorkGoogle Scholar
  42. Parks, T.W., Burrus, C.S. (1987): Digital Filter Design John Wiley and Sons, New YorkGoogle Scholar
  43. Pascual-Marqui, R.D., Michel, C.M., Lehmann, D. (1994): Low resolution electromagnetic tomography: a new method for localising electrical activity in the brain, Int. J. Psychophysiol. 18, 49–65CrossRefGoogle Scholar
  44. Perlitz, V., Schmid-Schönbein, H., Schulte, A., Dolgner, J., Petzold, E.R., Kruse, W. (1995): Effektivität des autogenen Trainings, Therapiewoche 26, 1536–1544Google Scholar
  45. Pikovsky, A.S., Rosenblum, M.G., Kurths, J. (1996): Synchronization in a population of globally coupled chaotic oscillators, Europhys. Lett. 34, 165–170Google Scholar
  46. Rosenblum, M., Kurths, J. (1998): Analysing synchronization phenomena from bi-variate data by means of the Hilbert transform. In: Nonlinear Analysis of Physiological Data, Kantz, H., Kurths, J., Mayer-Kress, G. (eds.), Springer, Berlin, 91–99CrossRefGoogle Scholar
  47. Rosenblum, M.G., Pikovsky, A.S., Kurths, J. (1996): Phase Synchronization of Chaotic Oscillators, Phys. Rev. Lett. 76, 1804–1807ADSCrossRefGoogle Scholar
  48. Rosenblum, M.G., Firsov G.I., Kuuz, R.A., Pompe, B. (1998): Human Postural Control: Force Plate Experiments and Modelling. In: Nonlinear Analysis of Physiological Data, Kantz, H., Kurths, J., Mayer-Kress, G. (eds.), Springer, Berlin, 283–306CrossRefGoogle Scholar
  49. Schäfer, C., Rosenblum, M.G., Kurths, J., Abel, H.-H. (1998): Heartbeat Synchronized with Ventilation, Nature 392, 239–240ADSCrossRefGoogle Scholar
  50. Schmid-Schönbein, H., Ziege, S. (1991): The high pressure system of the mammalian circulation as a dynamic self-organizing system. In: Haken and Koepchen ( 1991 ), pp. 77–96Google Scholar
  51. Schmid-Schönbein, H., Ziege, S., Bütten, W., Heidtmann, H. (1992): Active and passive modulation of cutaneous red cell flux as measured by Laser Doppler anemometry, Vasa 32, 38–47 (Suppl.)Google Scholar
  52. Selesnick, I.W., Lang, M., Burrus, C.S. (1996): Constraned Least Square Design of FIR Filters without Specified Transition Bands, IEEE Transactions on Signal Processing 44, 1879–1892ADSCrossRefGoogle Scholar
  53. Singer, W. (1989): Search for coherence: a basic principle of cortical self-organization, Concepts Neurosci. 1, 1–26Google Scholar
  54. Singer, W., Gray, C.M. (1995): Visual feature integration and the temporal correlation hypothesis, Annu. Rev. Neurosci. 18, 555–586CrossRefGoogle Scholar
  55. Steriade, H., Jones, E.G., Llinas, R. (1990): Thalamic Oscillations and Signaling, John Wiley and Sons, New YorkGoogle Scholar
  56. Stratonovich, R.L. (1963): Topics in the Theory of Random Noise, Gordon and Breach, New YorkGoogle Scholar
  57. Tass, P., Kurths, J., Rosenblum, M.G., Guasti, G., Hefter, H. (1996): Delay-induced transitions in visually guided movements, Phys. Rev. E 54, R2224 - R2227Google Scholar
  58. Tass, P., Rosenblum, M.G., Weule, J., Kurths, J., Pikovsky, A., Volkmann, J., Schnitzler, A., Freund, H.-J. (1998): Detection of n: m phase locking from noisy data: Application to magnetoencephalography, Phys. Rev. Lett. 81, 3291–3294ADSCrossRefGoogle Scholar
  59. Toga, A.W., Mazziotta, J.C. (eds.) (1996): Brain Mapping - The Methods, Academic Press, San DiegoGoogle Scholar
  60. Uhl, C., Kruggel, F., Opitz, B., Yves von Cramon, D. (1998): A New Concept for EEG/MEG Signal Analysis: Detection of Interacting Spatial Modes, Human Brain Mapping 6, 137–149CrossRefGoogle Scholar
  61. von Holst, E. (1935): Über den Prozess der zentralnervösen Koordination, Pflügers Archiv 236, 149–158CrossRefGoogle Scholar
  62. von Holst, E. 1939 ): Die relative Koordination als Phänomen und als Methode zentralnervöser Funktionsanalysen, Erg. Physiol. 42, 228–306Google Scholar
  63. Wang, J., Williamson, S.J., Kaufman, L. (1995): Spatio-temporal model of neural activity of the human brain based on the MNLS inverse. In: Biomagnetism: Fundamental Research and Clinical Applications, Baumgartner, C., Deeke, L., Stroink, G., Williamson, S.J. (eds.), Elsevier Science Publishers, Amsterdam, 299–301Google Scholar
  64. Winfree, A.T. (1980): The Geometry of Biological Time, Springer, BerlinzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Peter A. Tass
    • 1
  1. 1.Neurologische KlinikHeinrich-Heine-UniversitätDüsseldorfGermany

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