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

arXiv: https://arxiv.org/abs/2111.10026 · HTML: https://mlsanity.com/paper/2111.10026/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 79 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks](https://mlsanity.com/paper/1901.06523/) (2019)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal](https://mlsanity.com/paper/1808.10848/) (2018)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [Multi-channel EEG recordings during a sustained-attention driving task](https://mlsanity.com/paper/1809.06534/) (2018)
- [EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications](https://mlsanity.com/paper/2001.11337/) (2020)
- [Feature Learning from Incomplete EEG with Denoising Autoencoder](https://mlsanity.com/paper/1410.0818/) (2014)
- [Deep Network for Capacitive ECG Denoising](https://mlsanity.com/paper/1903.12536/) (2019)
- [Automated Pipeline for EEG Artifact Reduction (APPEAR) Recorded during fMRI](https://mlsanity.com/paper/1912.05507/) (2019)

## Built on by (most cited first, 10 of 10)

- [DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse](https://mlsanity.com/paper/2312.09417/) (2023)
- [EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG](https://mlsanity.com/paper/2409.18973/) (2024)
- [EEG Artifact Detection and Correction with Deep Autoencoders](https://mlsanity.com/paper/2502.08686/) (2025)
- [CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction](https://mlsanity.com/paper/2210.05988/) (2022)
- [Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review](https://mlsanity.com/paper/2311.01425/) (2023)
- [ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals](https://mlsanity.com/paper/2409.07326/) (2024)
- [IMU-Enhanced EEG Motion Artifact Removal with Fine-Tuned Large Brain Models](https://mlsanity.com/paper/2509.01073/) (2025)
- [SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG](https://mlsanity.com/paper/2512.05500/) (2025)
- [Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning](https://mlsanity.com/paper/2605.06724/) (2026)
- [Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks](https://mlsanity.com/paper/2610.16264/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent misinterpretations of neural signals and underperformance of brain-computer interfaces. This study developed a new artifact removal method, IC-U-Net, which is based on the U-Net architecture for removing pervasive EEG artifacts and reconstructing brain sources. The IC-U-Net was trained using mixtures of brain and non-brain sources decomposed by independent component analysis and employed an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain sources and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noises) was demonstrated in a simulation study and three real-world EEG datasets collected at rest and while driving and walking. IC-U-Net is user-friendly and publicly available, does not require parameter tuning or artifact type designations, and has no limitations on channel numbers. Given the increasing need to image natural brain dynamics in a mobile setting, IC-U-Net offers a promising end-to-end solution for automatically removing artifacts from EEG recordings.