# Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond

arXiv: https://arxiv.org/abs/2210.06295 · HTML: https://mlsanity.com/paper/2210.06295/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 131 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Revisiting Batch Normalization For Practical Domain Adaptation](https://mlsanity.com/paper/1603.04779/) (2016)
- [A DIRT-T Approach to Unsupervised Domain Adaptation](https://mlsanity.com/paper/1802.08735/) (2018)
- [Self-training for Few-shot Transfer Across Extreme Task Differences](https://mlsanity.com/paper/2010.07734/) (2020)
- [Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning](https://mlsanity.com/paper/1801.07756/) (2018)
- [Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN](https://mlsanity.com/paper/2205.13943/) (2022)
- [Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks](https://mlsanity.com/paper/1901.06958/) (2019)
- [Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining](https://mlsanity.com/paper/2204.12440/) (2022)
- [Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction](https://mlsanity.com/paper/2008.11426/) (2020)
- [Exploring Localization for Self-supervised Fine-grained Contrastive Learning](https://mlsanity.com/paper/2106.15788/) (2021)
- [Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders](https://mlsanity.com/paper/2009.13453/) (2020)
- [Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests](https://mlsanity.com/paper/1511.01258/) (2015)
- [Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition](https://mlsanity.com/paper/1912.11037/) (2019)
- [Expectation maximization transfer learning and its application for bionic hand prostheses](https://mlsanity.com/paper/1711.09256/) (2017)
- [Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification](https://mlsanity.com/paper/2112.15019/) (2021)
- [Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation](https://mlsanity.com/paper/2202.12598/) (2022)
- [Transfer Learning for sEMG-based Hand Gesture Classification using Deep Learning in a Master-Slave Architecture](https://mlsanity.com/paper/2005.03460/) (2020)

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

- [EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography](https://mlsanity.com/paper/2410.23625/) (2024)
- [Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings](https://mlsanity.com/paper/2410.12866/) (2024)
- [Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder](https://mlsanity.com/paper/2311.14675/) (2023)
- [Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference](https://mlsanity.com/paper/2505.15381/) (2025)
- [NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis](https://mlsanity.com/paper/2507.20189/) (2025)

## Abstract (arXiv)

Machine learning on electromyography (EMG) has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the assumption that the training and future data must be of the same data distribution. However, this assumption may not hold in many real-world applications. Model calibration is required via data re-collection and label annotation, which is generally very expensive and time-consuming. To address this problem, transfer learning (TL), which aims to improve target learners' performance by transferring the knowledge from related source domains, is emerging as a new paradigm to reduce the amount of calibration effort. In this survey, we assess the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications. Unlike previous surveys on purely transfer learning or EMG-based machine learning, this survey aims to provide an insight into the biological foundations of existing transfer learning methods on EMG-related analysis. In specific, we first introduce the physiological structure of the muscles and the EMG generating mechanism, and the recording of EMG to provide biological insights behind existing transfer learning approaches. Further, we categorize existing research endeavors into data based, model based, training scheme based, and adversarial based. This survey systematically summarizes and categorizes existing transfer learning approaches for EMG related machine learning applications. In addition, we discuss possible drawbacks of existing works and point out the future direction of better EMG transfer learning algorithms to enhance practicality for real-world applications.