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Cited by 88EEG and Brain-Computer InterfacesBlind Source Separation TechniquesECG Monitoring and Analysis

IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal

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Chuang, Chun-Hsiang, Chang, Kong-Yi, Huang, Chih-Sheng, Jung, Tzyy-Ping·2022·NeuroImage
DOI 10.1016/j.neuroimage.2022.119586
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Abstract

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end-to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting. The code and pre-trained IC-U-Net model are available at https://github.com/roseDwayane/AIEEG.

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Sorted by citations

Authors
Chuang, C.-H., Chang, K.-Y., Huang, C.-S., & Jung, T.-P.
Affiliations
  • National Tsing Hua University (TW)
  • National Taiwan Ocean University (TW)
  • National Taipei University of Technology (TW)
  • University of California San Diego (US)
Corresponding author
Chuang, Chun-Hsiang
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2022-11
Volume
263
Pages
119586
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2022.119586
OpenAlex
W3217407136
Cited by
88
References
3
Citations by year
2026: 26 · 2025: 29 · 2024: 22 · 2023: 10 · 2022: 1
FWCI
7.99
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • Ministry of Science and Technology, Taiwan (109-2636-E-007-022, 110-2636-E-007-018)
  • National Tsing Hua University
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Keywords
EEG artifact removal, U-Net, denoising autoencoder, independent component analysis, line noise, channel noise, brain-computer interface, wearable EEG
MeSH terms
Algorithms, Blinking, Electroencephalography, Eye Movements, Humans, Signal Processing, Computer-Assisted, Artifacts

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