# A novel convolutional neural network model to remove muscle artifacts from EEG

arXiv: https://arxiv.org/abs/2010.11709 · HTML: https://mlsanity.com/paper/2010.11709/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [Embedding Decomposition for Artifacts Removal in EEG Signals](https://mlsanity.com/paper/2112.00989/) (2021)
- [Geometric Machine Learning on EEG Signals](https://mlsanity.com/paper/2502.05334/) (2025)
- [DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse](https://mlsanity.com/paper/2312.09417/) (2023)
- [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)
- [Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)](https://mlsanity.com/paper/2502.05332/) (2025)
- [ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals](https://mlsanity.com/paper/2409.07326/) (2024)
- [D4PM: A Dual-branch Driven Denoising Diffusion Probabilistic Model with Joint Posterior Diffusion Sampling for EEG Artifacts Removal](https://mlsanity.com/paper/2509.14302/) (2025)
- [A Statistical Mixture-of-Experts Framework for EMG Artifact Removal in EEG: Empirical Insights and a Proof-of-Concept Application](https://mlsanity.com/paper/2509.19385/) (2025)
- [Real-time Noise Detection and Classification in Single-Channel EEG: A Lightweight Machine Learning Approach for EMG, White Noise, and EOG Artifacts](https://mlsanity.com/paper/2509.26058/) (2025)
- [Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning](https://mlsanity.com/paper/2605.06724/) (2026)
- [DenoFlow: Flow Matching for SSVEP Denoising under Real Physiological Artifacts](https://mlsanity.com/paper/2610.08817/) (2026)

## Abstract (arXiv)

The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts. In recent years, deep learning models have been used for denoising of electroencephalography (EEG) data and provided comparable performance with that of traditional techniques. However, the performance of the existing networks in electromyograph (EMG) artifact removal was limited and suffered from the over-fitting problem. Here we introduce a novel convolutional neural network (CNN) with gradually ascending feature dimensions and downsampling in time series for removing muscle artifacts in EEG data. Compared with other types of convolutional networks, this model largely eliminates the over-fitting and significantly outperforms four benchmark networks in EEGdenoiseNet. Our study suggested that the deep network architecture might help avoid overfitting and better remove EMG artifacts in EEG.