MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals

MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural n...

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Bibliographic Details
Main Authors: Md Shafayet Hossain, Sakib Mahmud, Amith Khandakar, Nasser Al-Emadi, Farhana Ahmed Chowdhury, Zaid Bin Mahbub, Mamun Bin Ibne Reaz, Muhammad E. H. Chowdhury
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Bioengineering
Subjects:
electroencephalogram (EEG)
electrooculogram (EOG)
electromyogram (EMG)
artifacts
denoising
MultiResUNet3+
Online Access:https://www.mdpi.com/2306-5354/10/5/579

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