Dynamics of Electroencephalogram Entropy and Pitfalls of Scaling Detection

Description:

This article discusses dynamics of electroencephalogram entropy and pitfalls of scaling detection. Herein the authors study the time evolution of diffusion entropy to elucidate the scaling of EGG time series.

Creator(s):
Creation Date: March 10, 2010
Partner(s):
UNT College of Arts and Sciences
Collection(s):
UNT Scholarly Works
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Total Uses: 126
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Creator (Author):
Ignaccolo, Massimiliano

Duke University

Creator (Author):
Latka, Miroslaw

Wroclaw University of Technology

Creator (Author):
Jernajczyk, Wojciech

Institute of Psychiatry and Neurology

Creator (Author):
Grigolini, Paolo

University of North Texas

Creator (Author):
West, Bruce J.

United States, Army Research Office

Publisher Info:
Publisher Name: American Physical Society
Place of Publication: [College Park, Maryland]
Date(s):
  • Creation: March 10, 2010
Description:

This article discusses dynamics of electroencephalogram entropy and pitfalls of scaling detection. Herein the authors study the time evolution of diffusion entropy to elucidate the scaling of EGG time series.

Degree:
Department: Physics
Note:

Copyright 2010 American Physical Society. The following article appeared in Physical Review E, 81:3; http://pre.aps.org/abstract/PRE/v81/i3/e031909

Note:

Abstract: In recent studies a number of research groups have determined that human electroencephalograms (EEG) have scaling properties. In particular, a crossover between two regions with different scaling exponents has been reported. Herein the authors study the time evolution of diffusion entropy to elucidate the scaling of EGG time series. For a cohort of 20 awake healthy volunteers with closed eyes, the authors find that the diffusion entropy of EEG increments (obtained from EEG waveforms by differencing) exhibits three features: short-time growth, an alpha wave related oscillation whose amplitude gradually decays in time, and asymptotic saturation which is achieved after approximately 1 s. This analysis suggests a linear, stochastic Ornstein-Uhlenbeck Langevin equation with a quasiperiodic forcing (whose frequency and/or amplitude may vary in time) as the model for the underlying dynamics. This model captures the salient properties of EEG dynamics. In particular, both the experimental and simulated EEG time series exhibit short-time scaling which is broken by a strong periodic component, such as alpha waves. The saturation of EEG diffusion entropy precludes the existence of asymptotic scaling. We find that the crossover between two scaling regions seen in detrended fluctuation analysis (DFA) of EEG increments does not originate from the underlying dynamics but is merely an artifact of the algorithm. This artifact is rooted in the failure of the "trend plus signal" paradigm of DFA.

Physical Description:

9 p.

Language(s):
Subject(s):
Keyword(s): diffusion entropy | electroencephalograms
Source: Physical Review E, 2010, College Park: American Physical Society
Partner:
UNT College of Arts and Sciences
Collection:
UNT Scholarly Works
Identifier:
  • DOI: 10.1103/PhysRevE.81.031909 |
  • ARK: ark:/67531/metadc40408
Resource Type: Article
Format: Text
Rights:
Access: Public
Citation:
Publication Title: Physical Review E
Volume: 81
Issue: 3
Peer Reviewed: Yes