Parameter Selection and Optimization in Brain Wave Research

  • Bernard Saltzberg


The increasingly important role of mathematical methods and computer analysis in brain wave research has created an urgent need to effectively communicate mathematical and computational concepts to neurophysiologists and other medical investigators engaged in behavioral and clinical research. I hope this paper will partly serve that need. The material presented is not intended as a mathematically rigorous treatment of parameter abstraction, but rather as a neurophysiologically motivated introduction to the mathematical notions underlying the selection and measurement of certain parameters. The ultimate objective is the correlation of these parameters with neurophysiological factors as a basis for studying the electrical activity of the brain and behavior.


Power Spectral Density Autocorrelation Function Parameter Selection Evoke Potential Zero Crossing 
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.

Suggested Reading

  1. Barnett, T.P., Johnson, L.C, Naitoh, P., Hicks, N., and Nute, C. 1971. Bispectrum analysis of electroencephalogram signals during waking and sleeping. Science 172:401.PubMedCrossRefGoogle Scholar
  2. Blackman, R.B., and Tukey, J.W. 1958. The Measurement of Power Spectra. New York: Dover.Google Scholar
  3. Bogert, B., Healy, M., and Tukey, J. 1963. The frequency analysis of time series for echoes. In M. Rosenblatt Ed.), Proceedings of Symposium on Time Series Analysis, p. 209. New York: Wiley.Google Scholar
  4. Callaway, E., and Halliday, R.A. 1973. Evoked potential variability: effects of age, amplitude and methods of measurement. Electroencephalogr. Clin. Neurophysiol. 34:125.PubMedCrossRefGoogle Scholar
  5. Hinich, M. 1962. A model for a self-adapting filter. Information Control 5:185.CrossRefGoogle Scholar
  6. Hinich, M. 1965. Large-sample estimation of an unknown discrete waveform which is randomly repeating in gaussian noise. Ann. Math. Statist. 36(2):489.CrossRefGoogle Scholar
  7. Hinich, M. 1967. Detection of an unknown waveform randomly recurring in gaussian noise. Information Control 10(4):394.CrossRefGoogle Scholar
  8. Noll, A.M. 1967. Cepstrum pitch determination. J. Acoust. Soc. Am. 41:293.PubMedCrossRefGoogle Scholar
  9. Oppenheim, A.V. 1965. Superposition in a class of non-linear systems. Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Mass. Technical Report 432.Google Scholar
  10. Oppenheim, A.V. 1965. Optimum homomorphic filters. Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Mass. Quarterly Progress Reports 77:248.Google Scholar
  11. Oppenheim, A.V. 1966. Non-linear filtering of convolved signals. Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Mass. Quarterly Progress Reports 80:168.Google Scholar
  12. Oppenheim, A.V., Schafer, R.W., and Stockham, Jr., T.G. 1968. Non-linear filtering of multiplied and convolved signals. Proc. IEEE 56:1264.CrossRefGoogle Scholar
  13. Robinson, E.A. 1957. Predictive decomposition of seismic traces. Geophysics 22:767.CrossRefGoogle Scholar
  14. Robinson, E.A. 1959. An Introduction to Infinitely Many Variates. New York: Hafner.Google Scholar
  15. Ruchkin, D.S. 1965. An analysis of average response computations based upon aperiodic stimuli. IEEE Trans. Biomed. Eng., BME-12:81.CrossRefGoogle Scholar
  16. Saltzberg, B. 1971. Digital filters in neurological research. Proceedings Symposium on Digital Filtering. London: Imperial College of Science and Technology.Google Scholar
  17. Saltzberg, B. 1973. Analysis of developmental electrophysiology. Tulane University School of Medicine, New Orleans, La. Annual Progress Report NINDS Grant 09332.Google Scholar
  18. Saltzberg, B., and Burch, N.R. 1971. Period analytic estimates of moments of the power spectrum: a simplified EEG time domain procedure. Electroencephalogr. Clin. Neurophysiol. 30:568.PubMedCrossRefGoogle Scholar
  19. Saltzberg, B., Lustick, L.S., and Heath, R.G. 1971. Detection of focal depth spiking in the scalp EEG of monkeys. Electroencephalogr. Clin. Neurophysiol. 31:327.CrossRefGoogle Scholar
  20. Schafer, R.W. 1967. Echo removal by generalized linear filtering. NEREM Record p. 118.Google Scholar
  21. Senmoto, S., and Childers, D.G. 1972. Adaptive decomposition of a composite signal of identical unknown wavelets in noise. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2, 1:59.Google Scholar
  22. Siegel, S. 1956. Nonparametric Statistics. New York: McGraw-Hill.Google Scholar

Copyright information

© Plenum Press, New York 1975

Authors and Affiliations

  • Bernard Saltzberg
    • 1
  1. 1.Department of Psychiatry and NeurologyTulane University School of MedicineNew OrleansUSA

Personalised recommendations