# A multi-artifact EEG denoising by frequency-based deep learning

arXiv: https://arxiv.org/abs/2310.17335 · HTML: https://mlsanity.com/paper/2310.17335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [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)
- [A novel convolutional neural network model to remove muscle artifacts from EEG](https://mlsanity.com/paper/2010.11709/) (2020)
- [EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer](https://mlsanity.com/paper/2109.04235/) (2021)
- [Embedding Decomposition for Artifacts Removal in EEG Signals](https://mlsanity.com/paper/2112.00989/) (2021)
- [Removal of Ocular Artifacts in EEG Using Deep Learning](https://mlsanity.com/paper/2209.11980/) (2022)

## Built on by (most cited first, 1 of 1)

- [hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications](https://mlsanity.com/paper/2312.00799/) (2023)

## Abstract (arXiv)

Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Under this setting, we tackle the challenge of distinguishing neurological activity from noise-related sources. We develop a novel EEG denoising model that operates in the frequency domain, leveraging prior knowledge about noise spectral features to adaptively compute optimal convolutional filters for noise separation. The model is trained to learn an empirical relationship connecting the spectral characteristics of noise and noisy signal to a non-linear transformation which allows signal denoising. Performance evaluation on the EEGdenoiseNet dataset shows that the proposed model achieves optimal results according to both temporal and spectral metrics. The model is found to remove physiological artifacts from input EEG data, thus achieving effective EEG denoising. Indeed, the model performance either matches or outperforms that achieved by benchmark models, proving to effectively remove both muscle and ocular artifacts without the need to perform any training on the particular type of artifact.