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 15, 2026

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

Deep-learning based electroencephalogram denoising: A literature review.

Le Wu1, Aiping Liu2, Jane Wang3

  • 1University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, P.R.China., Hefei, Anhui, 230026, China.

Journal of Neural Engineering
|July 13, 2026
PubMed
Summary

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

Synergistic Decoupling of Distance and Velocity in a Triboelectric Touchless Sensor for Intelligent Identity Recognition.

ACS applied materials & interfaces·2026
Same author

Generating R2<sup>*</sup> maps from T1W and T2W images using image-to-image translation for Parkinson's disease.

Medical physics·2026
Same author

The discovery of novel 3-nitropyridin-2-amine-based 2,3-dihydroimidazo[1,2-a]pyridin-5(1H)-one insecticides.

Pest management science·2026
Same author

The Discovery of HNPC-A0073: A Novel Thiazol-5-ylmethyl-Based 3-Nitropyridin-2-amine Fungicide.

Journal of agricultural and food chemistry·2026
Same author

Lineage tracing of soma-to-primordial germ cell-like conversion in human tumor cell line.

iScience·2026
Same author

Antibacterial activity and multi-target mechanism of naringenin against Alicyclobacillus acidoterrestris and its application in apple juice.

International journal of food microbiology·2026

Deep learning significantly improves electroencephalography (EEG) signal denoising by addressing artifacts. This review analyzes deep learning pipelines to enhance EEG data reliability and practical applications.

Area of Science:

  • Neuroscience
  • Signal Processing
  • Artificial Intelligence

Background:

  • Electroencephalography (EEG) is crucial for brain function research.
  • EEG signals are prone to artifacts (e.g., electromyogram, electrocardiogram, electrical noise) that impede analysis.
  • Existing deep learning methods show promise in EEG denoising but lack comprehensive review.

Purpose of the Study:

  • To bridge the literature gap by reviewing deep learning-based EEG denoising strategies.
  • To analyze the end-to-end denoising pipeline's influence on model performance and utility.
  • To identify future research directions for improved EEG denoising systems.

Main Methods:

  • Framework analysis of the end-to-end denoising pipeline.
  • Examination of data/target construction, input representation, architecture, objective design, and evaluation.
Keywords:
ArtifactBrain-computer InterfaceDeep learningDenoiseElectroencephalography

Related Experiment Videos

Last Updated: Jul 15, 2026

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

  • Discussion of selective/multi-task denoising, downstream validation, and model deployment.

    • Main Results:

    • Deep learning approaches offer competitive reconstruction fidelity and artifact suppression.
    • Pipeline components critically influence model assumptions, performance interpretation, and practical utility.
    • Translating reconstruction performance to usable applications requires careful consideration of validation and deployment.

    Conclusions:

    • A comprehensive review of deep learning for EEG denoising is needed.
    • Optimizing denoising pipelines is key to enhancing EEG data reliability.
    • Future work should focus on developing interpretable and practical EEG denoising solutions.