Indications of nonlinearity, intraindividual specificity and stability of human EEG: The unfolding dimension

Schmid, Gary Bruno; Dünki, Rudolf M.

doi:10.1016/0167-2789(95)00300-2

Cited by 21 publications

(14 citation statements)

References 61 publications

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“…The univariate case (ANOVA) tests the influence of one or more treatment effects upon the outcome of a single variable, for example, how the nonlinear correlation-dimension estimate b 0 [9] is affected by group, mental situation, and proband (cf. Section 4).…”

Section: Recapitulation Of the F-ratio Testmentioning

confidence: 99%

F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

Dünki

Dressel

2011

Journal of Biophysics

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Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ 2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ 2(F)/F 2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.

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Section: Recapitulation Of the F-ratio Testmentioning

confidence: 99%

F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

Dünki

Dressel

2011

Journal of Biophysics

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“…In this regard, we have chosen to compare and contrast the so-called si-milarity approach [1,2] with the nonlinear unfolding dimension approach proposed recently [3,4]. Some progress concerning the stable intraindividual specificity of EEG time series has already been made by advanced linear methods on healthy individuals between the ages of circa 25-35 years.…”

Section: Scope Of the Analysismentioning

confidence: 99%

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

Dünki

Schmid²,

Stassen³

2000

Methods Inf Med

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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.

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“…[Casdagli et al, 1996;Schmid & Dünki, 1996;Vibe & Vesin, 1996] and the references therein). Estimating these quantities and making uneqivocal classifications can prove difficult and the method of surrogate data [Theiler et al, 1992] is often employed to provide some rigor and certainty.…”

Section: Introductionmentioning

confidence: 99%

Detecting Nonlinearity in Experimental Data

Small

Judd

1998

Int. J. Bifurcation Chaos

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The technique of surrogate data has been used as a method to test for membership of particular classes of linear systems. We suggest an obvious extension of this to classes of nonlinear parametric models and demonstrate our methods with respiratory data from sleeping human infants. Although our data are clearly distinct from the different classes of linear systems we are unable to distinguish between our data and surrogates generated by nonlinear models. Hence we conclude that human respiration is likely to be a nonlinear system with more than two degrees of freedom with a limit cycle that is driven by high dimensional dynamics or noise.

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Indications of nonlinearity, intraindividual specificity and stability of human EEG: The unfolding dimension

Cited by 21 publications

References 61 publications

F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

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

Detecting Nonlinearity in Experimental Data

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