# Artifact Detection and Correction in EEG data: A Review

arXiv: https://arxiv.org/abs/2106.13081 · HTML: https://mlsanity.com/paper/2106.13081/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [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)
- [EEG Channel Interpolation Using Deep Encoder-decoder Netwoks](https://mlsanity.com/paper/2009.12244/) (2020)

## Built on by (most cited first, 4 of 4)

- [Feature Imitating Networks](https://mlsanity.com/paper/2110.04831/) (2021)
- [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)
- [EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training](https://mlsanity.com/paper/2512.12210/) (2025)
- [Brain-Gen: Towards Interpreting Neural Signals for Stimulus Reconstruction Using Transformers and Latent Diffusion Models](https://mlsanity.com/paper/2512.18843/) (2025)

## Abstract (arXiv)

Electroencephalography (EEG) has countless applications across many of fields. However, EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts contribute to the noisiness of EEG, and many techniques have been proposed to detect and correct these artifacts. These techniques range from simply detecting and rejecting artifact ridden segments, to extracting the noise component from the EEG signal. In this paper we review a variety of recent and classical techniques for EEG data artifact detection and correction with a focus on the last half-decade. We compare the strengths and weaknesses of the approaches and conclude with proposed future directions for the field.