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

Identifying phase synchronization clusters in spatially extended dynamical systems.

Stephan Bialonski1, Klaus Lehnertz

  • 1Department of Epileptology, Neurophysics Group, University of Bonn, Sigmund-Freud-Strasse 25, D-53105 Bonn, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 7, 2007
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

From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis.

Scientific reports·2026
Same author

Impaired sleep microarchitecture is associated with locus coeruleus degeneration in Parkinson's disease.

Parkinsonism & related disorders·2026
Same author

Introduction to Focus Issue: Nonautonomous dynamical systems: Theory, methods, and applications.

Chaos (Woodbury, N.Y.)·2026
Same author

Transcript-based estimators for characterizing interactions.

Chaos (Woodbury, N.Y.)·2026
Same author

AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts.

ArXiv·2025
Same author

Noise Robustness of Transcript-Based Estimators for Properties of Interactions.

Entropy (Basel, Switzerland)·2025
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

We explored two new methods for analyzing complex systems, finding they can detect synchronization patterns in data. These techniques, especially eigenvalue decomposition, accurately identify clusters in simulations and real-world EEG data.

Area of Science:

  • Complex Systems Analysis
  • Nonlinear Dynamics
  • Time Series Analysis

Background:

  • Detecting phase synchronization clusters is crucial for understanding spatially extended, nonstationary systems.
  • Existing methods may not fully capture complex collective dynamics in such systems.

Purpose of the Study:

  • To investigate two novel multivariate time series analysis techniques for detecting phase synchronization clusters.
  • To evaluate the robustness and accuracy of these methods in simulated and real-world applications.

Main Methods:

  • Utilized a mean-field approach to quantify subsystem participation in single synchronization clusters.
  • Employed eigenvalue decomposition to derive a participation index for identifying involvement in multiple synchronization clusters.
  • Simulated clusters in coupled Lorenz oscillators and analyzed multichannel EEG recordings from epilepsy patients.

Related Experiment Videos

Main Results:

  • The mean-field approach demonstrated robustness even when the single-cluster assumption was not fully met.
  • The eigenvalue-decomposition approach successfully identified simulated clusters, even with weak coupling.
  • Analysis of EEG data confirmed findings from established neurophysiological techniques, validating the method's clinical relevance.

Conclusions:

  • Both investigated methods offer valuable tools for analyzing complex systems.
  • Eigenvalue decomposition shows particular promise for studying spatiotemporal synchronization in biological systems like the brain.
  • Multivariate time series analysis, accounting for nonlinearities, provides deeper insights into collective system dynamics.