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 Video

Updated: Jul 10, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
10:56

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

Detecting behavioral microsleeps from EEG power spectra.

Malik R Peiris1, Richard D Jones, Paul R Davidson

  • 1Van der Veer Institute for Parkinson's and Brain Research, Christchurch, New Zealand. malik.peiris@vanderveer.org.nz

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
Summary

This study explored using electroencephalography (EEG) spectral power to detect brief behavioral microsleeps (BMs). Researchers developed a model that achieved modest but novel detection of BMs at a 1-second resolution.

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

Comparison of the properties of two-dimensional and three-dimensional percolating networks of nanoparticles.

Physical review. E·2025
Same author

Barracuda: a dynamic, Turing-complete GPU virtual machine for high-performance simulations.

Medical & biological engineering & computing·2025
Same author

Complexity of brain-like signals in self-organised nanoscale networks.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Conscious but not thinking-Mind-blanks during visuomotor tracking: An fMRI study of endogenous attention lapses.

Human brain mapping·2024
Same author

Cerebral perfusion is not impaired in persons with moderate obstructive sleep apnoea when awake.

Sleep & breathing = Schlaf & Atmung·2024
Same author

Experimental Demonstration of Reservoir Computing with Self-Assembled Percolating Networks of Nanoparticles.

Advanced materials (Deerfield Beach, Fla.)·2024

Area of Science:

  • Neuroscience
  • Sleep Science
  • Signal Processing

Background:

  • Electroencephalography (EEG) spectral power is linked to human arousal and alertness levels.
  • Detecting brief episodes of reduced alertness, known as behavioral microsleeps (BMs), is crucial for safety-critical tasks.
  • Previous methods for BMs detection lack high temporal resolution.

Purpose of the Study:

  • To evaluate the efficacy of EEG spectral power for detecting behavioral microsleeps (BMs).
  • To develop and assess a machine learning model for high-resolution BMs detection.

Main Methods:

  • Recorded EEG and facial video from eight participants performing a continuous tracking task.
  • Identified BMs visually from video recordings, independent of task performance.

More Related Videos

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

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Related Experiment Videos

Last Updated: Jul 10, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
10:56

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

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

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

  • Calculated EEG spectral power features using the Burg method across 16 derivations.
  • Reduced feature dimensionality with Principal Component Analysis (PCA).
  • Developed subject-specific classifiers using Linear Discriminant Analysis (LDA).
  • Combined classifiers using stacked generalization for an overall detection model.
  • Validated model performance using N-fold cross-validation.

    • Main Results:

    • The developed stacked generalization model achieved a detection performance of Phi=0.30 +/- 0.05.
    • This represents a modest but significant advancement in BMs detection.
    • The model successfully detected BMs at a high temporal resolution of 1 second.

    Conclusions:

    • EEG spectral power analysis, combined with machine learning, can detect behavioral microsleeps (BMs).
    • The achieved 1-second temporal resolution for BMs detection is a novel advancement.
    • Further research may refine this approach for practical applications in monitoring alertness.