# Deep learning denoising for EOG artifacts removal from EEG signals

arXiv: https://arxiv.org/abs/2009.08809 · HTML: https://mlsanity.com/paper/2009.08809/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Confidence Aware Neural Networks for Skin Cancer Detection](https://mlsanity.com/paper/2107.09118/) (2021)
- [EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network](https://mlsanity.com/paper/2404.15289/) (2024)
- [Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis](https://mlsanity.com/paper/2107.08514/) (2021)
- [Removal of Ocular Artifacts in EEG Using Deep Learning](https://mlsanity.com/paper/2209.11980/) (2022)
- [WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound](https://mlsanity.com/paper/2311.10857/) (2023)
- [E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices](https://mlsanity.com/paper/2602.09035/) (2026)

## Abstract (arXiv)

There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis since such artifacts cause many problems in EEG signals analysis. One of the most challenging issues in EEG denoising processes is removing the ocular artifacts where Electrooculographic (EOG), and EEG signals have an overlap in both frequency and time domains. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. In the proposed scheme, we convert each EEG signal to an image to be fed to a U-NET model, which is a deep learning model usually used in image segmentation tasks. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals similar to the process used in the image segmentation process. The results confirm that one of our schemes can achieve a reliable and promising accuracy to reduce the Mean square error between the target signal (Pure EEGs) and the predicted signal (Purified EEGs).