The Dynamics of EEG Entropy Metadata

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Title

  • Main Title The Dynamics of EEG Entropy

Creator

  • Author: Ignaccolo, Massimiliano
    Creator Type: Personal
    Creator Info: Duke University
  • Author: Latka, Miroslaw
    Creator Type: Personal
    Creator Info: Wroclaw University of Technology
  • Author: Jernajczyk, Wojciech
    Creator Type: Personal
    Creator Info: Institute of Psychiatry and Neurology
  • Author: Grigolini, Paolo
    Creator Type: Personal
    Creator Info: University of North Texas
  • Author: West, Bruce J.
    Creator Type: Personal
    Creator Info: Duke University; United States. Army Research Office

Publisher

  • Name: Springer-Verlag
    Place of Publication: [Berlin, Germany]

Date

  • Creation: 2009-03-05

Language

  • English

Description

  • Content Description: This article discusses the dynamics of EEG entropy.
  • Physical Description: 6 p.

Subject

  • Keyword: electroencephalography
  • Keyword: entropy
  • Keyword: neurophysiology
  • Keyword: brain diseases

Source

  • Journal: Journal of Biological Physics, 2010, Berlin: Springer-Verlag

Citation

  • Publication Title: Journal of Biological Physics
  • Volume: 36
  • Issue: 2
  • Page Start: 185
  • Page End: 196
  • Peer Reviewed: True

Collection

  • Name: UNT Scholarly Works
    Code: UNTSW

Institution

  • Name: UNT College of Arts and Sciences
    Code: UNTCAS

Rights

  • Rights Access: public

Resource Type

  • Article

Format

  • Text

Identifier

  • Archival Resource Key: ark:/67531/metadc132967

Degree

  • Academic Department: Physics
  • Academic Department: Center for Nonlinear Science

Note

  • Display Note: This is the pre-published version of this article. The final, definitive version can be found online: http://link.springer.com/article/10.1007%2Fs10867-009-9171-y
  • Display Note: Abstract: EEG time series are analyzed using the diffusion entropy method. The resulting EEG entropy manifests short-time scaling, asymptotic saturation and an attenuated alpha-rhythm modulation. These properties are faithfully modeled by a phenomenological Langevin equation interpreted within a neural network context.