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Power-law statistics of neurophysiological processes analyzed using short signals
Author(s): Olga N. Pavlova; Anastasiya E. Runnova; Alexey N. Pavlov
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Paper Abstract

We discuss the problem of quantifying power-law statistics of complex processes from short signals. Based on the analysis of electroencephalograms (EEG) we compare three interrelated approaches which enable characterization of the power spectral density (PSD) and show that an application of the detrended fluctuation analysis (DFA) or the wavelet-transform modulus maxima (WTMM) method represents a useful way of indirect characterization of the PSD features from short data sets. We conclude that despite DFA- and WTMM-based measures can be obtained from the estimated PSD, these tools outperform the standard spectral analysis when characterization of the analyzed regime should be provided based on a very limited amount of data.

Paper Details

Date Published: 26 April 2018
PDF: 5 pages
Proc. SPIE 10717, Saratov Fall Meeting 2017: Laser Physics and Photonics XVIII; and Computational Biophysics and Analysis of Biomedical Data IV, 107172D (26 April 2018); doi: 10.1117/12.2311477
Show Author Affiliations
Olga N. Pavlova, Saratov State Univ. (Russian Federation)
Anastasiya E. Runnova, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Alexey N. Pavlov, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Saratov State Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 10717:
Saratov Fall Meeting 2017: Laser Physics and Photonics XVIII; and Computational Biophysics and Analysis of Biomedical Data IV
Vladimir L. Derbov; Dmitry Engelevich Postnov, Editor(s)

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