Download PDF
Methods Inf Med 2000; 39(01): 78-82
DOI: 10.1055/s-0038-1634249
Original Article
Schattauer GmbH

Intraindividual Specificity and Stability of Human EEG: Comparing a Linear vs a Nonlinear Approach

R. M. Dünki
1   Computer Assisted Physics Group, University of Zürich, Switzerland
,
G. B. Schmid
2   General Psychiatry, Cantonal Psychiatric Clinic, Rheinau, Switzerland
,
H. H. Stassen
3   Psychiatric University Hospital, Research Dept., Zürich, Switzerland
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

    Permissions and Reprints

Abstract:

We have applied the so-called “unfolding dimension approach’’ to reanalyze an earlier longitudinal EEG study. Both linear and nonlinear approaches show that the EEG comprises a static, person-specific part upon which nonstatic and state-specific parts are superimposed. The intraindivi-dual specificity and stability of the genetic part are similar between methods. This is assessed by comparing the outcome of a person to his own outcomes at later times (14 days and 5 years later). The nonlinear method revealed a median correlation coefficient = 0.55, whereas advanced linear methods showed a median = 0.84. An apparent effect for the 5-year interval was detected with the nonlinear method and is discussed in terms of the different assumptions of the two approaches concerning EEG signal generation.

Keywords:

Dimension Analysis - Similarity Approach - EEG Stability - Power Spectrum
 

  • REFERENCES

  • 1 Stassen HH. The similarity approach to EEG analysis. Method Inform Med 1985; 24: 200-12.
    PubMedSearch in Google Scholar
  • 2 Stassen HH, Lykken DT, Propping P, Bomben G. Genetic determination of human EEG. Survey of recent results on twins reared together and apart. Hum Gen 1988; 80: 165-76.
    CrossrefPubMedSearch in Google Scholar
  • 3 Schmid GB, Dünki RM. Indications of nonlinearity, intraindividual specificity and stability of human EEG: The unfolding dimension. Physica D 1996; 93: 163-90.
    PubMedSearch in Google Scholar
  • 4 Dünki RM, Schmid GB. Unfolding dimension and the search for functional markers in the human electroencephalogram. Phys Rev E 1998; 57: 2115-22.
    CrossrefPubMedSearch in Google Scholar
  • 5 Stassen HH. The octave approach to EEG analysis. Method Inform Med 1991; 30: 304-10.
    PubMedSearch in Google Scholar
  • 6 Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica D 1983; 9: 189-208.
    CrossrefPubMedSearch in Google Scholar
  • 7 Theiler J, Galdrikian J, Longtin A, Eubank S, Farmer JD. Using surrogate data to detect nonlinearity in time series. In: Casdagli M, Eubank S. eds. Nonlinear Modeling and Forecasting. Reading MA: Addison Wesley; 1992: 164-88.
    Search in Google Scholar
  • 8 Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer JD. Testing for non-linearity in time series: the method of surrogate data. Physica D 1992; 58: 77-94.
    CrossrefPubMedSearch in Google Scholar
  • 9 Dumermuth G. Numerical spectral analysis of the electroencephalogram. In Rémond A. ed. Handbook of Electroencephalography and Clinical Neurophysiology. Amsterdam: Elsevier Science Publ; 1973: 33-60.
    Search in Google Scholar
  • 10 Takens F. Detecting strange attractors in turbulence. In: Rand DA, Young LS. eds. Dynamical Systems and Turbulence . Berlin: Springer; 1981: 366-81.
    Search in Google Scholar