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

You might also read

Related Articles

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

Sort by
Same author

A broken power-law model of heart rate variability spectra in sleep.

Computers in biology and medicine·2026
Same author

Spectral Features of Heart Rate Variability in Williams Syndrome During Sleep.

Journal of clinical medicine·2026
Same author

EEG microstates reveal distinct network dynamics in lucid and non-lucid REM sleep.

Consciousness and cognition·2026
Same author

An Increase in C-Reactive Protein Levels during Antidepressant Treatment as a Candidate Marker for Treatment Nonresponse in Major Depressive Disorder.

Neuropsychobiology·2026
Same author

The Young Adult Sleep model: an evolving causal loop diagram of mental health dynamics.

BMC medicine·2026
Same author

Cheating hypnos: can polyphasic sleep schedules reduce the need for sleep?

Sleep·2026

Related Experiment Video

Updated: May 28, 2025

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.6K

Comparing Manual and Automatic Artifact Detection in Sleep EEG Recordings.

Péter P Ujma1, Martin Dresler2, Róbert Bódizs1

  • 1Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.

Psychophysiology
|February 9, 2025
PubMed
Summary

Sleep electroencephalogram (EEG) artifacts minimally impact average power spectrum density (PSD) estimates. Automatic artifact detection effectively removes distortions, making manual inspection unnecessary for large sleep EEG datasets.

Keywords:
EEGartifactsautomatic data processingdata qualitysleep

More Related Videos

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K
Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

751

Related Experiment Videos

Last Updated: May 28, 2025

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.6K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K
Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

751

Area of Science:

  • Neuroscience
  • Signal Processing

Background:

  • Sleep electroencephalogram (EEG) data frequently contain artifacts that can distort analysis.
  • Both visual inspection and automatic methods are used to identify and remove artifactual EEG segments.

Purpose of the Study:

  • To systematically evaluate the impact of artifacts on sleep EEG power spectrum density (PSD).
  • To compare the effectiveness of visual artifact detection against a simple automatic method using Hjorth parameters.
  • To determine if manual artifact removal is essential for accurate sleep EEG analysis.

Main Methods:

  • Systematic exploration of artifact effects on sleep EEG PSD.
  • Comparison of gold-standard visual artifact detection with an automatic detector using Hjorth parameters.
  • Analysis of all-night average PSD across different artifact detection methods.

Main Results:

  • Most distortions in average PSD are caused by a small number of severe artifacts, primarily affecting beta, gamma, and NREM delta frequencies.
  • Visual and automatic artifact detection methods showed only moderate agreement.
  • Despite detection differences, all methods yielded highly similar all-night average PSDs, preserving known age and sex correlations.
  • Accurate PSD estimates can be obtained from a fraction of the data epochs.

Conclusions:

  • Artifacts in sleep EEG recordings pose a minor and solvable problem.
  • Visual inspection of EEG data for artifact removal is not strictly necessary.
  • Automatic artifact detection methods are sufficient for accurate sleep EEG analysis, especially for large databases.