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: May 14, 2026

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
11:00

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

What does clean EEG look like?

Ian Daly1, Floriana Pichiorri, Josef Faller

  • 1Institute for Knowledge Discovery, Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37/IV, 8010, Graz, Austria i.daly@tugraz.at

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

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

Effective Connectivity-based Unsupervised Channel Selection Method for Electroencephalography.

Journal of medical signals and sensors·2026
Same author

EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Uses of assistive technology incorporating smart camera features in the rehabilitation of people living with disabilities: a scoping review.

Disability and rehabilitation. Assistive technology·2026
Same author

Improving P300 morphology through single-trial latency realignment: a comparative study of template-matching approaches.

Journal of neural engineering·2026
Same author

Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.

IEEE transactions on cybernetics·2026
Same author

EEG Correlates of Upper Limb Function During the Box and Block Test After Stroke.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

A new algorithm identifies clean electroencephalography (EEG) signals by analyzing statistical properties. This method improves artifact removal and boosts confidence in EEG analysis across diverse patient groups.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Reliable electroencephalography (EEG) analysis is hindered by artifacts, complicating comparisons of artifact removal techniques.
  • A standardized metric for identifying artifact-free EEG data is currently lacking, impacting research reproducibility.
  • Existing methods for EEG artifact removal vary, leading to inconsistent results and reduced confidence in findings.

Purpose of the Study:

  • To develop a robust analytical metric for identifying clean electroencephalography (EEG) epochs.
  • To establish a reliable method for assessing the quality of EEG signals.
  • To enhance the comparability of different EEG artifact removal strategies.

Main Methods:

  • An algorithm was developed to identify clean EEG epochs by thresholding statistical properties.

More Related Videos

Recording Brain Activity with Ear-Electroencephalography
09:58

Recording Brain Activity with Ear-Electroencephalography

Published on: March 31, 2023

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

Related Experiment Videos

Last Updated: May 14, 2026

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
11:00

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

Recording Brain Activity with Ear-Electroencephalography
09:58

Recording Brain Activity with Ear-Electroencephalography

Published on: March 31, 2023

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

  • Differential evolution (DE) was employed to train thresholds on EEG datasets.
  • The algorithm was validated using EEG data from both healthy individuals and patients with stroke or spinal cord injury.

    • Main Results:

    • The proposed algorithm effectively identifies artifact-free EEG epochs.
    • Thresholds trained via differential evolution demonstrated efficacy across diverse populations.
    • The developed metric provides a quantitative basis for assessing EEG signal cleanliness.

    Conclusions:

    • The presented algorithm offers a reliable solution for identifying clean EEG signals.
    • This metric facilitates more robust comparisons of EEG artifact removal methods.
    • The findings increase confidence in the results derived from EEG analyses, particularly in clinical populations.