Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Predictability of human EEG: a dynamical approach.

D Gallez1, A Babloyantz

  • 1Université Libre de Bruxelles, Service de Chimie Physique, Belgium.

Biological Cybernetics
|January 1, 1991
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Pacemaker-Induced Coherence in Cortical Networks.

Neural computation·2019
Same author

Pattern segmentation in a binary/analog world: unsupervised learning versus memory storing.

Neural networks : the official journal of the International Neural Network Society·2000
Same author

Ionic strength dependence of localized contact formation between membranes: nonlinear theory and experiment.

Biophysical journal·1999
Same author

Brain chaos and computation.

International journal of neural systems·1996
Same author

Control of spatiotemporal chaos in neuronal networks.

International journal of neural systems·1996
Same author

Computation with chaos: a paradigm for cortical activity.

Proceedings of the National Academy of Sciences of the United States of America·1994

This study analyzed electroencephalogram (EEG) signals using nonlinear dynamics, revealing chaotic patterns in brain activity. Chaos and entropy increase from coma to deep sleep to alpha waves, indicating varying information processing rates.

Area of Science:

  • Neuroscience
  • Nonlinear Dynamics
  • Complexity Science

Background:

  • Electroencephalogram (EEG) recordings provide insights into brain activity.
  • Nonlinear dynamics offers advanced methods for analyzing complex biological signals.
  • Understanding brain states like alpha waves, deep sleep, and coma is crucial for neuroscience.

Purpose of the Study:

  • To analyze human scalp EEG recordings using nonlinear dynamics.
  • To quantify brain activity in different states (alpha waves, deep sleep, coma) using dynamical parameters.
  • To investigate the presence and degree of chaos in these brain states.

Main Methods:

  • Analysis of EEG signals within the framework of nonlinear dynamics.
  • Evaluation of dynamical parameters: Lyapunov exponents and Kolmogorov entropy.

Related Experiment Videos

  • Calculation of attractor dimensions using Lyapunov exponents and comparison with the Grassberger-Procaccia algorithm.

    • Main Results:

    • Presence of at least two positive Lyapunov exponents, indicating chaos in all analyzed brain states.
    • Increase to three positive Lyapunov exponents for alpha waves, suggesting increased variability over time.
    • A clear trend of increasing entropy/chaos from coma to deep sleep to alpha waves.
    • Deep sleep shows a large predicting time, implying slow information processing; alpha waves have a smaller predicting time, indicating rapid information loss.

    Conclusions:

    • EEG signals exhibit chaotic dynamics across different brain states.
    • The degree of chaos and entropy varies significantly between states, reflecting different information processing capacities.
    • Nonlinear dynamical analysis provides valuable metrics for characterizing brain activity and its complexity.