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

Dynamics of brain electrical activity.

P E Rapp1, T R Bashore, J M Martinerie

  • 1Department of Physiology and Biochemistry, Medical College of Pennsylvania, Philadelphia 19129.

Brain Topography
|January 1, 1989
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

Speed pressure in conflict situations impedes inhibitory action control in Parkinson's disease.

Biological psychology·2014
Same author

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures.

Cognitive neurodynamics·2014
Same author

The effect of speed-accuracy strategy on response interference control in Parkinson's disease.

Neuropsychologia·2009
Same author

The effect of Parkinson's disease on interference control during action selection.

Neuropsychologia·2008
Same author

Comment on "Performance of different synchronization measures in real data: a case study on electroencephalographic signals".

Physical review. E, Statistical, nonlinear, and soft matter physics·2005
Same author

Comparative study of embedding methods.

Physical review. E, Statistical, nonlinear, and soft matter physics·2005
Same journal

Diffusion-Informed Joint Segmentation Enhances Detection of Thalamic Atrophy in Parkinson's Disease.

Brain topography·2026
Same journal

Local Field Potential Recordings Using Deep Brain Stimulation: A Practical Workflow and Open-Source Signal Processing Pipeline.

Brain topography·2026
Same journal

Electrocortical Indices of Default Mode Network-Related Activity in ADHD and Modulation Through Mindfulness-Based Cognitive Therapy.

Brain topography·2026
Same journal

Electroencephalogram for the Diagnosis of Depression: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.

Brain topography·2026
Same journal

Mapping Whole-Brain Nonlinear Structure-Function Dynamics in Aging via Neural Granger Causality.

Brain topography·2026
Same journal

Association Between Spatiotemporal Properties of Global Brain Activity and Childhood Emotional and Behavioral Problems: Evidence from Microstate C.

Brain topography·2026
See all related articles

Dynamical systems theory methods, like correlation dimension estimation, were applied to human electroencephalographic (EEG) signals and event-related brain potentials (ERPs). While challenging with noisy data, this technique offers insights into brain activity changes related to cognition.

Area of Science:

  • Dynamical Systems Theory
  • Neuroscience
  • Biophysics

Background:

  • Dynamical systems theory offers analytical techniques for experimental data.
  • Applications have expanded from physical and chemical systems to biological systems.
  • Human electroencephalography (EEG) and event-related potentials (ERPs) are complex biological signals.

Purpose of the Study:

  • To apply correlation dimension estimation from dynamical systems theory to characterize human EEG and ERP signals.
  • To assess the feasibility and challenges of using this technique on noisy biological data.
  • To explore if this method can complement classical analyses of brain activity.

Main Methods:

  • Estimation of a signal's correlation dimension.
  • Application of this technique to human electroencephalographic (EEG) signals.

Related Experiment Videos

  • Analysis of event-related brain potentials (ERPs).

    • Main Results:

    • Substantial technical difficulties were encountered when estimating dimensions from noisy biological signals.
    • The correlation dimension estimation procedure provided partial characterization of cerebral electrical activity changes.
    • The results suggest this method complements classical analytical procedures.

    Conclusions:

    • Estimating correlation dimension from EEG and ERP signals is technically challenging due to noise.
    • Despite challenges, this method shows potential for characterizing changes in brain activity.
    • The technique offers a complementary approach to understanding cognitive behavior-related brain activity.