# EEG Artifact Detection and Correction with Deep Autoencoders

arXiv: https://arxiv.org/abs/2502.08686 · HTML: https://mlsanity.com/paper/2502.08686/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal](https://mlsanity.com/paper/2111.10026/) (2021)
- [Embedding Decomposition for Artifacts Removal in EEG Signals](https://mlsanity.com/paper/2112.00989/) (2021)
- [CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction](https://mlsanity.com/paper/2210.05988/) (2022)

## Built on by (most cited first, 2 of 2)

- [Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms](https://mlsanity.com/paper/2504.08469/) (2025)
- [Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks](https://mlsanity.com/paper/2610.16264/) (2026)

## Abstract (arXiv)

EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate analysis and interpretation. Traditional EEG artifact removal methods, while effective, often require extensive expert intervention. This study presents LSTEEG, a novel LSTM-based autoencoder designed for the detection and correction of artifacts in EEG signals. Leveraging deep learning, particularly LSTM layers, LSTEEG captures non-linear dependencies in sequential EEG data. LSTEEG demonstrates superior performance in both artifact detection and correction tasks compared to other state-of-the-art convolutional autoencoders. Our methodology enhances the interpretability and utility of the autoencoder's latent space, enabling data-driven automated artefact removal in EEG its application in downstream tasks. This research advances the field of efficient and accurate multi-channel EEG preprocessing, and promotes the implementation and usage of automated EEG analysis pipelines for brain health applications.