# TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement

arXiv: https://arxiv.org/abs/2410.03843 · HTML: https://mlsanity.com/paper/2410.03843/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 90 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Quantization Networks](https://mlsanity.com/paper/1911.09464/) (2019)
- [MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement](https://mlsanity.com/paper/2104.03538/) (2021)
- [Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data](https://mlsanity.com/paper/1709.07902/) (2017)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder](https://mlsanity.com/paper/1610.04019/) (2016)
- [TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain](https://mlsanity.com/paper/2103.09963/) (2021)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [Contextual Attention Network: Transformer Meets U-Net](https://mlsanity.com/paper/2203.01932/) (2022)
- [Raw Waveform-based Speech Enhancement by Fully Convolutional Networks](https://mlsanity.com/paper/1703.02205/) (2017)
- [SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation](https://mlsanity.com/paper/2103.03604/) (2021)
- [On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement](https://mlsanity.com/paper/1909.01019/) (2019)
- [An empirical study of Conv-TasNet](https://mlsanity.com/paper/2002.08688/) (2020)
- [Feature Learning from Incomplete EEG with Denoising Autoencoder](https://mlsanity.com/paper/1410.0818/) (2014)
- [ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks](https://mlsanity.com/paper/2210.13271/) (2022)
- [Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencoder](https://mlsanity.com/paper/2012.06275/) (2020)
- [U-shaped Transformer with Frequency-Band Aware Attention for Speech Enhancement](https://mlsanity.com/paper/2112.06052/) (2021)

## Built on by (most cited first, 6 of 6)

- [MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network](https://mlsanity.com/paper/2411.18902/) (2024)
- [MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution](https://mlsanity.com/paper/2412.04861/) (2024)
- [MID: A Self-supervised Multimodal Iterative Denoising Framework](https://mlsanity.com/paper/2511.00997/) (2025)
- [Translating Signals to Languages for sEMG-Based Activity Recognition](https://mlsanity.com/paper/2605.22403/) (2026)
- [SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising](https://mlsanity.com/paper/2609.11663/) (2026)
- [Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment](https://mlsanity.com/paper/2609.12724/) (2026)

## Abstract (arXiv)

Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remove sEMG contaminants, as non-invasive measurements render sEMG susceptible to various contaminants. However, these approaches often rely on heuristic-based optimization and are sensitive to the contaminant type. A more potent, robust, and generalized sEMG denoising approach should be developed for various healthcare and human-computer interaction applications. This paper proposes a novel neural network (NN)-based sEMG denoising method called TrustEMG-Net. It leverages the potent nonlinear mapping capability and data-driven nature of NNs. TrustEMG-Net adopts a denoising autoencoder structure by combining U-Net with a Transformer encoder using a representation-masking approach. The proposed approach is evaluated using the Ninapro sEMG database with five common contamination types and signal-to-noise ratio (SNR) conditions. Compared with existing sEMG denoising methods, TrustEMG-Net achieves exceptional performance across the five evaluation metrics, exhibiting a minimum improvement of 20%. Its superiority is consistent under various conditions, including SNRs ranging from -14 to 2 dB and five contaminant types. An ablation study further proves that the design of TrustEMG-Net contributes to its optimality, providing high-quality sEMG and serving as an effective, robust, and generalized denoising solution for sEMG applications.