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

Beyond the lab: a review on neurophysiological mental states assessment in real-world settings.

Progress in biomedical engineering (Bristol, England)·2026
Same author

Mapping the Human Performance Envelope Through Multivariate Information Transfer.

Brain sciences·2026
Same author

A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.

PloS one·2026
Same author

A novel neurophysiological approach to evaluate the impact of virtual training on skills acquisition.

Journal of neural engineering·2026
Same author

Brain Cortical Area Characterization and Machine Learning-Based Measure of Rasmussen's S-R-K Model.

Brain sciences·2025
Same author

Analysis of Neurophysiological Correlates of Mental Fatigue in Both Monotonous and Demanding Driving Conditions.

Brain sciences·2025

Related Experiment Video

Updated: Jun 9, 2025

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

10.6K

Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction.

Vincenzo Ronca1,2, Rossella Capotorto3, Gianluca Di Flumeri2,4

  • 1Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy.

Bioengineering (Basel, Switzerland)
|October 25, 2024
PubMed
Summary

This tutorial guides researchers on correcting ocular artifacts like blinks and saccades in electroencephalographic (EEG) data. It covers traditional and advanced methods to ensure accurate neurophysiological study results.

Keywords:
EEGocular artifactssignal processing

More Related Videos

Multiscale Investigations of Cortical Processing by Integrating Laminar Polytrodes and Optogenetics with Micro Electrocorticography in Rodents
07:52

Multiscale Investigations of Cortical Processing by Integrating Laminar Polytrodes and Optogenetics with Micro Electrocorticography in Rodents

Published on: May 23, 2025

71
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K

Related Experiment Videos

Last Updated: Jun 9, 2025

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

10.6K
Multiscale Investigations of Cortical Processing by Integrating Laminar Polytrodes and Optogenetics with Micro Electrocorticography in Rodents
07:52

Multiscale Investigations of Cortical Processing by Integrating Laminar Polytrodes and Optogenetics with Micro Electrocorticography in Rodents

Published on: May 23, 2025

71
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Ocular artifacts (blinks, saccades) significantly contaminate electroencephalographic (EEG) data.
  • These artifacts obscure vital neural signals, hindering accurate analysis in neurophysiological studies.
  • Effective artifact correction is crucial for reliable EEG data interpretation.

Purpose of the Study:

  • To provide a comprehensive tutorial on effective ocular artifact correction methods for EEG data.
  • To review and compare traditional and advanced artifact correction algorithms.
  • To equip researchers with tools for maintaining EEG data integrity in various settings.

Main Methods:

  • Review of traditional methods: regression-based techniques and Independent Component Analysis (ICA).
  • Exploration of advanced methods: Artifact Subspace Reconstruction (ASR) and deep learning algorithms.
  • Detailed step-by-step instructions and comparative analysis of correction strategies.

Main Results:

  • The tutorial offers a comparative analysis of various artifact correction techniques.
  • It provides practical guidance for implementing these methods.
  • Strategies are evaluated for their effectiveness in laboratory and real-world scenarios.

Conclusions:

  • Accurate ocular artifact correction is essential for reliable EEG data analysis.
  • The tutorial equips researchers with practical tools for robust EEG data processing.
  • The discussed methods are particularly relevant for wearable EEG and real-time applications.