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

Dünki, Rudolf M.; Dressel, M.

doi:10.1016/j.physa.2006.02.033

Cited by 6 publications

(9 citation statements)

References 43 publications

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“…Their likelihood ratio may be expressed through the ratio of their likelihood ratios against the null hypothesis [6]…”

Section: Hypothesis Weightingmentioning

confidence: 99%

“…Following [ 6 ], we rely (a) upon the classical error propagation rule and (b) upon the variance's variance. The error propagation rule is given as [ 13 ]…”

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

“…This is important, because p , the probability of a person to appear in a particular random sample, increases with the ratio #probands per random sample/#probands per sample. In case of a small sample size, this may impose an additional restriction of the variance σ 2 ( F ) [ 6 ].…”

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

“…One hereby chooses repeatedly random subsets of the original data to gain an estimate of the variance of F , namely, σ 2 ( F ), and inspects the ratios σ 2 ( F )/ F 2 or variants therefrom [6]. Here F denotes the quantity obtained from a F- test (cf.…”

Section: Introductionmentioning

confidence: 99%

“…As an alternative to this classical approach, one may consider the family of the so-called F- ratio tests which are based on randomly splitting and recollecting the sample. One hereby chooses repeatedly random subsets of the original data to gain an estimate of the variance of F , namely, σ 2 ( F ), and inspects the ratios σ 2 ( F )/ F 2 or variants therefrom [ 6 ]. Here F denotes the quantity obtained from a F- test (cf.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Their likelihood ratio may be expressed through the ratio of their likelihood ratios against the null hypothesis [6]…”

Section: Hypothesis Weightingmentioning

confidence: 99%

“…Following [ 6 ], we rely (a) upon the classical error propagation rule and (b) upon the variance's variance. The error propagation rule is given as [ 13 ]…”

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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