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 Concept Videos

Brain Waves01:23

Brain Waves

4.7K
Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:
4.7K

You might also read

Related Articles

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

Sort by
Same author

Lightweight Pre-Trained Korean Language Model Based on Knowledge Distillation and Low-Rank Factorization.

Entropy (Basel, Switzerland)·2025
Same author

Diffusion-Phys: noise-robust heart rate estimation from facial videos via diffusion models.

Biomedical engineering letters·2025
Same author

A Case of Nasolacrimal Duct Obstruction Caused by a Lacrimal Sac Retention Cyst.

Journal of rhinology : official journal of the Korean Rhinologic Society·2024
Same author

Application of operating scenarios and analysis of unstable flow characteristics at various angles of inlet guide vane.

Scientific reports·2024
Same author

Spiking neural networks for physiological and speech signals: a review.

Biomedical engineering letters·2024
Same author

Multiscale distribution entropy analysis of short epileptic EEG signals.

Mathematical biosciences and engineering : MBE·2024

Related Experiment Video

Updated: Apr 3, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K

Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation.

Young-Seok Choi1

  • 1Department of Electronic Engineering, Gangneung-Wonju National University, Gangneung 210-702, Republic of Korea ; Research Institute for Dental Engineering, Gangneung-Wonju National University, Gangneung 210-702, Republic of Korea.

Biomed Research International
|September 18, 2015
PubMed
Summary

This study introduces a novel multiscale Renyi entropy to analyze electroencephalogram (EEG) data. This new measure effectively quantifies information in EEG signals, aiding in diagnosing injury levels after cardiac arrest.

More Related Videos

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.8K
Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

13.0K

Related Experiment Videos

Last Updated: Apr 3, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.8K
Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

13.0K

Area of Science:

  • Neuroscience
  • Information Theory
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG) signals exhibit nonlinear and nonstationary characteristics across multiple time scales.
  • Previous entropy measures have limitations in capturing the complex dynamics of EEG over varying temporal resolutions.

Purpose of the Study:

  • To develop a data-driven multiscale entropy measure for quantifying information in EEG recordings.
  • To assess the utility of this novel measure in evaluating neurological injury following cardiac arrest.

Main Methods:

  • EEG data was decomposed using Empirical Mode Decomposition (EMD) to handle nonstationarity.
  • Renyi entropy was calculated for the probability distributions of intrinsic mode functions derived from EMD.
  • The proposed multiscale Renyi entropy was applied to EEG recordings from rats experiencing cardiac arrest and resuscitation.

Main Results:

  • The multiscale Renyi entropy demonstrated enhanced discriminative capability for assessing injury severity.
  • The measure showed improved correlation with neurological deficit evaluations 72 hours post-cardiac arrest.
  • Simulation and experimental results validated the proposed entropy measure's performance.

Conclusions:

  • The data-driven multiscale Renyi entropy is an effective tool for analyzing EEG signals.
  • This novel entropy measure shows promise as a diagnostic and prognostic indicator for neurological injury after cardiac arrest.