# Embedding Decomposition for Artifacts Removal in EEG Signals

arXiv: https://arxiv.org/abs/2112.00989 · HTML: https://mlsanity.com/paper/2112.00989/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 69 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks](https://mlsanity.com/paper/1901.06523/) (2019)
- [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)

## Built on by (most cited first, 5 of 5)

- [A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network](https://mlsanity.com/paper/2212.05289/) (2022)
- [EEG Artifact Detection and Correction with Deep Autoencoders](https://mlsanity.com/paper/2502.08686/) (2025)
- [A multi-artifact EEG denoising by frequency-based deep learning](https://mlsanity.com/paper/2310.17335/) (2023)
- [How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings](https://mlsanity.com/paper/2411.11886/) (2024)
- [BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal](https://mlsanity.com/paper/2604.24428/) (2026)

## Abstract (arXiv)

Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis. Here, we propose an deep learning framework to separate neural signal and artifacts in the embedding space and reconstruct the denoised signal, which is called DeepSeparator. DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal. Besides, DeepSeparator can extract the artifact, which largely increases the model interpretability. The proposed method is tested with a semi-synthetic EEG dataset and a real task-related EEG dataset, suggesting that DeepSeparator outperforms the conventional models in both EOG and EMG artifact removal. DeepSeparator can be extended to multi-channel EEG and data of any length. It may motivate future developments and application of deep learning-based EEG denoising. The code for DeepSeparator is available at https://github.com/ncclabsustech/DeepSeparator.