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Statistics of biophysical signal characteristics and state specificity of the human EEG

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  • Dünki, R.M.
  • Dressel, M.

Abstract

The analysis of records of biophysical signals like EEG have become increasingly interesting to physicists since the development of new algorithms for time-series analysis. This analysis, however, requires both—an adequate methodology assessing the time-series characteristics and an appropriate statistical assessment of the measures resulting from these algorithms. The latter should separate inherent effects from those appearing only by chance, .e.g., in terms of an appropriate test statistics. As a step towards such a statistical assessment we present here a data resampling approach: Data resampling techniques allow for the estimation of the variance of an F-value as obtained through variance analysis. A test statistics based on the so-called F-ratio σ2(F)/F2 is derived and the simulation of outcomes allows for the determination of its quantiles. A multivariate formulation allows for the simultaneous inclusion of linear and nonlinear time-series measures into the analysis.

Suggested Citation

  • Dünki, R.M. & Dressel, M., 2006. "Statistics of biophysical signal characteristics and state specificity of the human EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 632-650.
    • Handle: RePEc:eee:phsmap:v:370:y:2006:i:2:p:632-650
    DOI: 10.1016/j.physa.2006.02.033
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    References listed on IDEAS

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    1. Dünki, Rudolf M & Keller, Elvira & Meier, Peter F & Ambühl, Brigitte, 2000. "Temporal patterns of human behaviour: are there signs of deterministic 1/f scaling?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 276(3), pages 596-609.
    2. Hwa, Rudolph C & Ferree, Thomas C, 2004. "Stroke detection based on the scaling properties of human EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 246-254.
    3. Diambra, L. & Malta, C.P. & Capurro, A. & Fernández, J., 2001. "Nonlinear structures in electroencephalogram signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 300(3), pages 505-520.
    4. Giuliani, Alessandro & Colafranceschi, Mauro & Webber, Charles L & Zbilut, Joseph P, 2001. "A complexity score derived from principal components analysis of nonlinear order measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 567-588.
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    Cited by:

    1. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.

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