This page provides you with the source code and results of

Naro D, Rummel C, Schindler K, Andrzejak RG  (2014):  Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score. Phys. Rev. E. 90:032913

If you use these resources, please make sure that you give a reference to the manuscript. (The correct way to cite this paper is shown above, no page numbers should be given.)

In order to fully understand the following resources, you should read the manuscript. To find the pdf-file, please follow the link at the top of this page.

Source code

The source code NaroRummelSchindlerAndrzejak.m (link at the bottom of the page) calculates the amplitude-based nonlinear prediction error E and rank-based nonlinear prediction score S. It is commented and should not contain any bugs. If you find problems in it, please contact us. 

Results obtained for the Bern-Barcelona-EEG Database
The Matlab file ResultsNaro2014.mat (linked at the bottom of this page) contains four variables: Efocal, Enonfocal, Sfocal, and Snonfocal. All four variables are a matrix of 3750x20. The 3750 correspond to the 3750 individual EEG signals. The 20 corresponds to the 1 original (first entry) and 19 surrogates (entry 2 to 20). "E" stands for the amplitude-based nonlinear prediction error, and "S" stands for the rank-based nonlinear prediction score. The terms "focal" and "nonfocal" indicate just what the name suggests.
Very important note

If you want to reproduce the results for the original EEG time series exactly, you at first have to filter and the downsample the signal. The parameters are given in the manuscript and matlab routines for the filtering are provided along with the Bern-Barcelona-EEG Database. On the page of this database we also explain why the results for the stochastic surrogates will always be different (please see under "Important remark").

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The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.