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

1.9K
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:
1.9K

You might also read

Related Articles

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

Sort by
Same author

Predicting EEG seizures using graded spiking neural networks.

Journal of neural engineering·2025
Same author

How measurement noise limits the accuracy of brain-behaviour predictions.

Nature communications·2024
Same author

Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

Communications biology·2024
Same author

A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition.

eLife·2024
Same author

Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity.

Communications biology·2023
Same author

On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI.

Entropy (Basel, Switzerland)·2022

Related Experiment Video

Updated: Aug 29, 2025

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K

Temporal complexity of EEG encodes human alertness.

Mohammad Hadra1, Amir Omidvarnia2,3, Mostefa Mesbah4

  • 1Center for Preparatory Studies, Sultan Qaboos University, PO Box 33 PC 123, Al-Khoud, Muscat, Oman.

Physiological Measurement
|September 5, 2022
PubMed
Summary

Range entropy (RangeEn) effectively distinguishes human alertness states like awake, drowsy, and sleep using electroencephalography (EEG) data. This novel measure shows superior performance over approximate entropy (ApEn) and Sample Entropy (SampEn) for EEG temporal complexity analysis.

Keywords:
EEGapproximate entropyhuman alertnesshuman vigilancerange entropysample entropytemporal complexity

More Related Videos

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

33.9K
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

10.3K

Related Experiment Videos

Last Updated: Aug 29, 2025

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K
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

33.9K
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

10.3K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Automatic human alertness monitoring is crucial for safety in tasks like driving and critical system operation.
  • Electroencephalography (EEG) temporal complexity is a potential feature for assessing alertness states.
  • Existing entropy measures like approximate entropy (ApEn) and Sample Entropy (SampEn) have limitations in analyzing EEG complexity.

Purpose of the Study:

  • To investigate the efficacy of range entropy (RangeEn) in discriminating human alertness states (awake, drowsy, sleep).
  • To compare the performance of RangeEn against ApEn and Sample Entropy (SampEn) for EEG temporal complexity.
  • To explore the potential of EEG temporal complexity for developing automated alertness monitoring systems.

Main Methods:

  • Utilized EEG data from 60 healthy subjects across different ages during overnight sleep.
  • Computed RangeEn, ApEn, and SampEn using a 30-second sliding window on EEG signals.
  • Performed statistical analyses to assess the discriminative power of entropy measures for alertness states.

Main Results:

  • All three entropy measures (RangeEn, ApEn, SampEn) provided useful information on human alertness.
  • RangeEn demonstrated a higher discriminative capability compared to ApEn and SampEn.
  • The superior performance of RangeEn was particularly evident in the beta frequency band of EEG.

Conclusions:

  • EEG temporal complexity, particularly as measured by RangeEn, evolves significantly across human alertness states.
  • RangeEn shows promise as a key feature for developing advanced automatic human alertness monitoring systems.
  • These findings could aid in diagnosing neurological and sleep disorders, including insomnia.