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

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

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...

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Bibliographic Details
Main Authors: Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Tzyy-Ping Jung
Format: Article
Language:English
Published: Elsevier 2022-11-01
Series:NeuroImage
Subjects:
EEG
Artifact Removal
Signal Reconstruction
U-Net
Independent Component Analysis
ICLabel
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811922007017

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