Multichannel wavelet-type decomposition of evoked potentials: model-based recognition of generator activity

  • A. B. Geva
  • H. Pratt
  • Y. Y. Zeevi


Scalp recording of electrical events allows the evaluation of human cerebral function, but contributions of the specific brain structures generating the recorded activity are ambiguous. This problem is ill-posed and cannot be solved without physiological constraints based on the spatio-temporal characteristics of the generators' activity. In our model-based analysis of evoked potentials for the purpose of generator activity detection, multichannel scalp-recorded signals are decomposed into a combination of wavelets, each of which can describe the neural mass coherent activity of cell assemblies. Elimination of contributions of specific generators and/or distributed background activity can produce physiologically motivated time-frequency filtering. The decomposition and filtering procedures are demonstrated by three examples: simulation of the surface manifestation of known intracranial generators; decomposition and reconstruction of auditory brainstem evoked potentials which reflect the differences among generators of these potentials; and cognitive components of evoked potentials which are diminished in the averaged recording but are clearly detected in single-trial signals.


Bioelectric inverse problem Evoked potential source estimation Matching pursuit Model-based pattern recognition Wavelet-type decomposition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Achim, A., Richer, F., andSaint-Hilaire, J. M. (1991): ‘Méthodological considerations for the evaluation of spatio-temporal source models (STAM)’.Electroenceph. Clin. Neurophysiol.,79, pp. 227–240.CrossRefGoogle Scholar
  2. Bartnik, E. A., Blinowska K. J., andDurka P. J. (1992): ‘Single evoked potential reconstruction by mean of wavelet transform’,Biol. Cybern.,67, pp. 175–181MATHCrossRefGoogle Scholar
  3. Birch, G. E., Lawrence, P. D., andHare, R. D. (1993): ‘Single-trial processing of event-related potentials using outlier information’,IEEE Trans,BME-40 (1), pp. 159–73Google Scholar
  4. Daubechies, I. (1988). ‘Orthogonal bases of compactly supported wavelets’,Commun. Pure Appl. Math.,41, pp. 909–996MATHMathSciNetGoogle Scholar
  5. Donchin E. (1980). ‘Event-related brain potentials: a tool in the study of human information processing’, inBegleiter, H. (Ed.): ‘Evoked potentials in psychiatry’, (Plenum, New York)Google Scholar
  6. Duffy F. H. (1982). ‘Topographic-display of-evoked potentials: clinical applications of brain electrical activity mapping (BEAM). evoked potentials’,Ann. New York: Acad. Sci.,388, pp. 183–198Google Scholar
  7. Fender, D. H. (1987): ‘Source localization of brain electrical activity’, InGevins, A. S., andRemond, A. (Eds.): ‘Methods of analysis of brain electrical and magnetic signals: handbook of electroencephalography and clinical neurophysiology’, (Elsevier, Amsterdam), vol. 1, chap. 13, pp. 355–403.Google Scholar
  8. Genossar T., andPorat M. (1992): ‘Optimal bi-orthonormal approximation of singals.IEEE Trans,SMC-(3) Google Scholar
  9. Geva, A. B., Pratt, H., andZeevi, Y. Y. (1995): ‘Spatio-temporal multiple source localization by wavelet-type decomposition of evoked potentials’,Electroenceph. Clin. Neurophysiol., pp. 96278–96286Google Scholar
  10. Geva, A. B., Pratt, H., andZeevi, Y. Y. (1996): ‘Spatio-temporal source estimation of evoked potentials by wavelet-type decomposition’ inGath, I., andInbar, G. (Eds.), ‘Advances in processing and pattern analysis of biological signals’, Plenum, New York).Google Scholar
  11. Gorodnitsky, I. F., George, J. S., andRao B. D. (1995): ‘Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm’,Electroenceph. Clin. Neurophysiol.,95 pp. 231–251CrossRefGoogle Scholar
  12. Jasper, H. H. (1958): ‘The ten twenty electrode system of the international federation’,Electroenceph. Clin. Neurophysiol.,10 pp. 71–357Google Scholar
  13. Lehman D. (1986): ‘Spatial analysis of human evoked potential’ inCracco Q., andBodis-Wollner I. (Eds.): ‘Evoked potentials’ (Alan R. Liss, New York), Chap. 1, pp. 3–14Google Scholar
  14. Mallat, S. G. (1989): ‘A theory for multiresolution signal decomposition: the wavelet representation’,IEEE Trans. PAMI-11, (7), pp. 674–693MATHGoogle Scholar
  15. Mallat, S., andZhang, Z. (1993): ‘Matching pursuits with timefrequency dictionaries’,IEEE Trans SP-72, pp. 3397–3415Google Scholar
  16. McGillem, C. D., andAunon, J. I. (1987): ‘Analysis of event-related potentials’, inGevins, A. S., andRemond, A. (Eds.): ‘Methods and analysis of brain electrical and magnetic signals: EEG handbook’, (Elsevier, Amsterdam) vol. 1, pp. 131–169Google Scholar
  17. Morton, J., Marcus, S. andFrankish, C. (1976): ‘Perceptual centers (P-centers)’,Psychol. Rev.,83, pp. 405–408.CrossRefGoogle Scholar
  18. Mosher, J. C., L&Sewis, P. S., andLeahy, R. M. (1992): ‘Multiple dipole modeling and localization from-spatio-temporal MEG Data’,IEEE Trans.,BME-39 pp. 541–557Google Scholar
  19. Nunez, P. L. (1981) ‘Electric fields of the brain: the neurophysics of EEG, (Oxford, New York)Google Scholar
  20. Plonsey, R. andFleming, D. G. (1969): ‘Bioelectric phenomena’ (McGraw-Hill, New York), Chap. 5Google Scholar
  21. Plonsey, R., andBarr C. B. (1988): ‘Bioelectricity’ (Plenum Press, New York)Google Scholar
  22. Porat, M., andZeevi, Y. Y. (1988): ‘The generalized Gabor scheme of image representation in biological and machine vision’,IEEE Trans.,PAMI-10, 452–468MATHGoogle Scholar
  23. Pratt H., Michalewski, H. J., Barrett, G., andStarr, A. (1989): ‘Brain potentials in a memory-scanning task: I Modality and task effects on potentials to probes.Electroenceph Clin. Neurophysiol.,72, pp. 407–421CrossRefGoogle Scholar
  24. Pratt H., Martin, W. H., Schwegler, J. W., Rosenwasser, R. H., Rosenberg, S. L., andFlamm E. S. (1992): ‘Temporal correspondence of intracranial, cochlear and scalp-recorded human auditory nerve action potentials’,Electroenceph. Clin. Neurophysiol.,84, pp. 447–455CrossRefGoogle Scholar
  25. Regan, D. (1989): ‘Human brain electrophysiology evoked potentials and evoked magnetic fields in science and medicine’ (Elsevier, Amsterdam) pp. 57–66Google Scholar
  26. Rioul, O., andDuhamel, P. (1992): ‘Fast alogorithms for discrete and continuous wavelet transforms’,IEEE Trans.,IT-38, pp. 569–586MATHMathSciNetGoogle Scholar
  27. Scherg, M., andVon Cramon, D. (1985): A new interpretation of the generators of BAEP waves I–V: results of spatio-temporal dipole model’,Electroenceph. Clin. Neurophysiol.,62, pp. 290–299CrossRefGoogle Scholar
  28. Tichonov, A. N., andArsenin, V. Y. (1977): ‘Solution of ill-posed problems’, (Wiley, New York)Google Scholar

Copyright information

© IFMBE 1997

Authors and Affiliations

  • A. B. Geva
    • 1
  • H. Pratt
    • 2
  • Y. Y. Zeevi
    • 3
  1. 1.Department of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Evoked Potentials Laboratory, Faculty of Medicine, Technion-IsraelInstitute of TechnologyHaifaIsrael
  3. 3.Faculty of Electrical EngineeringTechnion-Israel Institute of TechnologyHaifaIsrael

Personalised recommendations