Brain Waves: Is Synchrony a Sign of Higher Function? Is the EEG Basically Rhythmic?

  • Theodore Holmes Bullock


Higher centers and higher animals seem to show more EEG synchrony than brainstem or nonmammalian forebrain. Yet sleeping and seizure states are thought to be excessively synchronized. In place of synchrony would some measure of extent and variety of forms of cooperativity correlate better with higher function? Despite more than 50 years of research, we still have little idea how much synchrony there is in the EEG, how that varies among states of the brain, parts of the brain and major groups of animals or how any other form of cooperativity is distributed among the diverse cellular and subcellular generators in a given volume. In most animals most of the time no obvious rhythms are widespread in the forebrain.

Among the opportunities and needs for EEG research, which include new attention to DC and infraslow shifts, not further developed here, I lift up the neglect of the semimicro-scale in space, from about 0.5 to 5 mm and the time domain from about 0.2 to 2 sec. I claim there is most probably an information-rich world in these dimensions. We urgently need many-channeled recording with wide-band amplifiers and novel analytical algorithms to tap this information, if not to decode and understand the local processing, then at least for descriptors that will reveal differences between states, parts and species. The EEG has evolved although we have recognized little evidence of it, aside from amplitude.

Our present methods are as limited as would be those of an anthropologist trying to distinguish commonalities from differences in the vocalizations of football crowds in Britain, bullfight crowds in Spain and chattering monkeys at the zoo by using microphones above the crowds and linear spectral analyses that treat epochs of several seconds as stationary. The analogy is chosen to include fluctuating tendencies for groups of individuals, scattered or all together, to synchronize their voices. Even given the advantage of synchrony, as distinct from the cocktail party, understanding depends on learning the rudiments of the languages; discerning the significant differences between the three kinds of sources depends on hearing many samples and learning the invariants. A parallel effort on the comparative study of brain activity, using high temporal and spatial resolution, on many samples, and human pattern recognizers in visual and auditory realms, pending the appropriate computer programs, might well turn up species-, state-and stage-specific features, or at least those which distinguish higher taxa (orders, classes and phyla) and major segments of the forebrain.

Scalp recordings cannot predict the EEG recorded intracerebrally or even subdurally, on the surface, since it filters in favor of low frequencies and in addition selects from the brain activity that which tends to be synchronous over the large cone of tissue “seen” by each electrode. Likewise, the subdural EEG cannot predict the intracortical micro-EEG which shows even more local sign. Evidence is cited to question the common view that the ongoing activity of the brain as recorded from the pial surface or from intracerebral electrodes is basically a mixture of sinusoidal oscillations. True rhythms may occur, such as alpha, theta and gamma waves in special conditions and places. The evidence for the usual micro-EEG points, instead, to a large number of small and local generators mainly emitting sharp-cornered events with broad spectra, such as synaptic potentials or abrupt transients that may, but need not be periodic.

Coherence of all bands in the subdural EEG tends to fall within millimeters to levels indistinguishable from noise; as it fluctuates with both space and time, all bands tend to decrease or increase in coherence together. Coherence for each band fluctuates with time, typically with from one to three apparent periods, usually in the range 0.3-0.03 Hz. Bispectrum and bicoherence analyses show that many frequencies are typically coupled, i.e. are much more often in phase than would be true by coincidence among independent oscillators but not unexpectedly if a significant part of the power comes from broad-band events which could be either transients or more or less regularly recurrent. The cross-bispectrum and cross-bicoherence results indicate, on preliminary evidence, that this form of nonlinear cooperativity among cellular generators constitutes a significant fraction of the cortical EEG power in both mammals and reptiles.


Power Spectrum Slow Wave Time Structure Humpback Whale Brain Wave 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abeles M (1991): Corticonics. Neural Network of the Cere- bral Cortex. Cambridge: Cambridge University PressCrossRefGoogle Scholar
  2. Achimowicz JZ (1989): Single evoked potentials extraction by pattern recognition in phase domain. In: Lecture Notes of the ICB Seminar, 1.1 Biosystems: Nervous System. Measurement and Analysis of Evoked Potentials and EMC, Hausmannowa-Petrusewicz I, Jagielski J, Tamecki R, eds. Warsaw: International Center for Biocybernetics, pp 430–436Google Scholar
  3. Adey WR (1969): Slow electrical phenomena in the central nervous system. Neurosci Res Prog Bull 7: 75–180Google Scholar
  4. Adrian ED and Matthews BHC (1934): The interpretation of potential waves in the cortex. JPhysiol (Lund) 81: 440–471Google Scholar
  5. Aertsen AMHJ, Gerstein GL (1991): Dynamic aspects of neuronal cooperativity: fast stimulus-locked modulations of effective connectivity. In: Neuronal Cooperativity, Krüger J, ed. Berlin: Springer-Verlag, pp 52–66CrossRefGoogle Scholar
  6. Allison T (1972): Comparative and evolutionary aspects of sleep. In: The Sleeping Brain. Perspectives in the Brain Sciences, Vol 1, Chase MH, ed. Los Angeles: Brain Infor- mation Service, Brain Research Institute, UCLA, pp 1–57Google Scholar
  7. Azanza MJ (1984): Bioelectric activity from isolated brain of Lacerta atlantica. Comp Biochem Physiol 79A: 175–178CrossRefGoogle Scholar
  8. Baiar E (1980): EEG-Brain Dynamics. Amsterdam: ElsevierGoogle Scholar
  9. Balar E (1988): Dynamics of Sensory and Cognitive Processing by the Brain. Berlin: Springer-VerlagGoogle Scholar
  10. Baiar E (1990): Chaos in Brain Function. Berlin: Springer-VerlagGoogle Scholar
  11. Baiar E, Bullock TH (1989): Brain Dynamics: Progress and Perspectives. Berlin: Springer-VerlagGoogle Scholar
  12. Creutzfeldt O, Houchin J (1974): Neuronal basis of EEG waves. In: Handbook of Electroencephalography and Clinical Neurophysiology, Vol 2, part C, Remond A, ed. Amsterdam: Elsevier, pp 5–55Google Scholar
  13. Danilova NN (1975): Neuronal mechanisms of synchronization and desynchronization of electrical activity of the brain. In: Neuronal Mechanisms of the Orienting Reflex, Sokolov EN, Vinogradova OS, eds. Hillsdale NJ: Lawrence Erlbaum Assoc, and New York: John Wiley and Sons, pp 178–199Google Scholar
  14. Eckhorn R, Bauer R, Jordan W, Brosch M, Kruse W, Munk M, Reitboeck FU (1988): Coherent oscillations: a mechanism of feature linking in the visual cortex? Multiple electrode and correlation analysis in the cat. Biol Cybern 60: 121–130CrossRefGoogle Scholar
  15. Eggermont JJ (1990): The Correlative Brain. (Studies of Brain Function Series, vol. 16 ). Berlin: Springer-VerlagCrossRefGoogle Scholar
  16. Enger PS (1957): The electroencephalogram of the codfish. Acta Physiol Scared 39: 55–72CrossRefGoogle Scholar
  17. Gardner WA (1992): A unifying view of coherence in signal processing. Signal Processing 29: 113–140CrossRefGoogle Scholar
  18. Gray CM, Singer W (1989): Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc Natl Acad Sci USA 86: 1698–1702CrossRefGoogle Scholar
  19. Hecht-Nielsen R (1991): Signal processing with neural networks—throwing off the yoke of linearity. Proceedings of SPIE Annual Symposium, San Diego CA. Bellingham WA: Optical Engineering SocietyGoogle Scholar
  20. Hobson JA (1967): Respiration and EEG synchronisation in the frog. Nature 213: 988–989CrossRefGoogle Scholar
  21. Jansen BH, Hasman A, Lenten R (1981): Piecewise analysis of EEGs using AR-modeling and clustering. Comput Biomed Res 14: 168–178CrossRefGoogle Scholar
  22. Kaminski MI, Blinowska KJ (1991): A new method of the description of the information flow in the brain structures. Biol Cybern 65: 203–210CrossRefGoogle Scholar
  23. Klemm WR (1969): Animal Electroencephalography. New York: Academic PressGoogle Scholar
  24. Kreiter AK, Aeretsen MHJ, Gerstein GL (1989): A low-cost single-board solution for real-time, unsupervised waveform classification of multineuron recordings. JNeurosci Methods 30: 59–69CrossRefGoogle Scholar
  25. Krüger J, Bach M (1981): Simultaneous recording with 30 microelectrodes in monkey visual cortex. Exp Brain Res 41: 191–194CrossRefGoogle Scholar
  26. Kryukov VI, Borisyuk GN, Borisyuk RM, Kirillov AB, Kovalenko YI (1990): Metastable and unstable states in the brain. In: Stochastic Cellular Systems: Ergodicity, Memory, Morphogenesis, Dobrushin RL, Kryukov VI, Toom AL, eds. Manchester: Manchester University Press, pp 225–357Google Scholar
  27. Kuperstein M, Eichenbaum H (1985): Unit activity, evoked potentials and slow waves in the rat hippocampus and olfactory bulb recorded with a 24-channel microelectrode. Neuroscience 15: 703–712CrossRefGoogle Scholar
  28. Laming PR (1980): Electroencephalographic studies on arousal in the goldfish (Carassius auratus). J Comp Physiol Psychol 94: 238–254CrossRefGoogle Scholar
  29. Laming PR (1981): The physiological basis of alert behaviour in fish. In: Brain Mechanisms of Behaviour in Lower Vertebrates, Laming PR, ed. Cambridge: Cambridge University Press, pp 203–224Google Scholar
  30. Laming PR (1982): Electroencephalographic correlates of behavior in the anurans, Bufo regularis and Rana temporaria. Behav Neural Biol 34: 296–306CrossRefGoogle Scholar
  31. Laming PR (1983): Relationships between the responses of visual units, EEGs and slow potential shifts in the optic tectum of the toad. In: Advances in Vertebrate Neuroethology, Ewert J-P, Capranica RR, Ingle DJ, eds. New York: Plenum Press, pp 595–602 (NATO ASI Series, Series A: Life Sciences, Vol 56 )Google Scholar
  32. Laming PR, Savage GE (1981): Seasonal differences in brain activity and responsiveness shown by the goldfish (Carassium auratus). Behav Neural Biol 32: 386–389CrossRefGoogle Scholar
  33. Lehmann D (1989): Microstates of the brain in EEG and ERP mapping studies. In: Brain Dynamics: Progress and Perspectives, Balar E, Bullock TH, eds. Berlin: Springer-Verlag, pp 72–83CrossRefGoogle Scholar
  34. Lopes da Silva F, Van RotterdamA(1982): Biophysical aspects of EEG and MEG generation. In: Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Niedermeyer E, Lopes da Silva F, eds. Baltimore: Urban and Scharzenberg, pp 15–26Google Scholar
  35. Lopes da Silva F (1991): Neural mechanisms underlying brain waves: from neural membranes to networks. Electroencephalogr Clin Neurophysiol 79: 81–93CrossRefGoogle Scholar
  36. Misczak J, Achimowicz JZ (1990): Taxonomic brain electric activity mapping for evaluation of central nervous system functional state. Second Int Symp: Imaging of the brain in psychiatry and relatedfields, Würzburg. Würzburg: International Society for Neuroimaging in Psychiatry, #77Google Scholar
  37. Mitzdorf U (1985): Visually and electrically evoked field potentials and current source densities in the cat visual cortex. In: Evoked Potentials. Neurophysiological and ClinicalAspects, Morocutti C, Rizzo PA, eds. Amsterdam: Elsevier, pp 273–279Google Scholar
  38. Morrell F, (1967): Electrical signs of sensory coding. In: The Neurosciences: A Study Program, Quarton GC, Melnechuk T, Schmitt FO, eds. New York: Rockefeller University Press, pp 452–469Google Scholar
  39. Mountcastle VB, Reitboeck HJ, Poggio GF, Steinmetz MA (1991): Adaptation of the Reitboeck method of multiple microelectrode recording to the neocortex of the waking monkey. J Neurosci Methods 36: 77–84CrossRefGoogle Scholar
  40. Ning T, Bronzino JD (1989): Bispectral analysis of the rat EEG during various vigilance states. IEEE Trans Biomed Eng 36: 497–499CrossRefGoogle Scholar
  41. Odell RH Jr, Smith SW, Yates FE (1975): Apermutation test for periodicities in short, noisy time series. Ann Biomed Eng 3: 160–180CrossRefGoogle Scholar
  42. Payne RS, McVay S (1971): Songs of humpback whales. Science 173: 587–597CrossRefGoogle Scholar
  43. Petsche H, Pockberger H, Rappelsberger P (1984): On the search for the sources of the electroencephalogram. Neuroscience 11: 1–27CrossRefGoogle Scholar
  44. Petsche H, Pockberger H, Rappelsberger P (1989): The micro-EEG: methods and application to the analysis of the antiepileptic action of benzodiazepines. In: EEG in Drug Research, Herrmann WM, ed. Stuttgart: Gustav Fischer, pp. 60–79Google Scholar
  45. Pickard RS (1979): Printed circuit microelectrodes. Trends Neurosci 2: 259–261CrossRefGoogle Scholar
  46. Praetorius HM, Bodenstein G, Creutzfeldt OD (1977): Adaptive segmentation of EEG records: a new approach to automatic EEG analysis. Electroencephalogr Clin Neurophysiol 42: 84–94CrossRefGoogle Scholar
  47. Prohaska O. Pacha F, Pfundner P, Petsche H (1979): A 16-fold semi-microelectrode for intracortical recordings of field potentials. Electroencephalogr Clin Neurophysiol 47: 629–631CrossRefGoogle Scholar
  48. Rappelsberger P, Petsche H (1988): Probability mapping: power and coherence analyses of cognitive processes. Brain Topogr 1: 46–54CrossRefGoogle Scholar
  49. Reitboeck HIP (1983): A 19-channel matrix drive with individually controllable fiber microelectrodes for neuro-physiological applications. IEEE Trans Systems, Man, Cybernetics SMC-13:676–682Google Scholar
  50. Schadé JP, Weiler PJ (1959): EEG patterns in goldfish. J Exp Biol 36: 435–452Google Scholar
  51. Scheuler W, Rappelsberger P, Schmatz F, Pastelak-Price C, Petsche H, Kubicki S (1990): Periodicity analysis of sleep EEG in the second and minute ranges—example of application in different alpha activities in sleep. Electroencephalogr Clin Neurophysiol 76: 222–234CrossRefGoogle Scholar
  52. Segura ET, De Juan A (1966): Electroencephalographic studies in toads. Electroencephalogr Clin Neurophysiol 21: 373–380CrossRefGoogle Scholar
  53. Sherman DL, Zoltowski MD (1989): Application of eigenstructure based bispectrum estimation: EEG wave coupling in cognitive tasks. In: Workshop on Higher-Order Spectral Analysis. The Lodge at Vail, June 28–30, 1989.Google Scholar
  54. Washington DC: Office of Naval Research and National Science Foundation (IEEE catalog no. 89TH0267–5)Google Scholar
  55. Thomsen CE; RosenfalckA; Notregaard CK (1991): Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms. Comput Methods Program Biomed 34: 125–38Google Scholar

Copyright information

© Springer Science+Business Media New York 1993

Authors and Affiliations

  • Theodore Holmes Bullock
    • 1
  1. 1.Department of Neurosciences 0201University of California, San DiegoLa JollaUSA

Personalised recommendations