# From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG

arXiv: https://arxiv.org/abs/2605.03462 · HTML: https://mlsanity.com/paper/2605.03462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 5 May 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [A DIRT-T Approach to Unsupervised Domain Adaptation](https://mlsanity.com/paper/1802.08735/) (2018)
- [Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering](https://mlsanity.com/paper/2003.08607/) (2020)
- [Cluster Alignment with a Teacher for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1903.09980/) (2019)
- [Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning](https://mlsanity.com/paper/1801.07756/) (2018)
- [Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model](https://mlsanity.com/paper/2502.10707/) (2025)
- [EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography](https://mlsanity.com/paper/2410.23625/) (2024)
- [Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition](https://mlsanity.com/paper/1912.11037/) (2019)
- [Cross-domain error minimization for unsupervised domain adaptation](https://mlsanity.com/paper/2106.15057/) (2021)

## Abstract (arXiv)

Surface electromyography provides a practical way to infer human movement intention from wearable muscle recordings, but models trained under a single acquisition setting often lose reliability when the user, session, electrode layout, or gesture protocol changes. This paper proposes AEMG, a self-supervised learning approach designed to extract reusable neuromuscular representations from diverse EMG sources. Eight public gesture datasets are first transformed into a shared signal format to reduce discrepancies in channel configuration, sensor topology, and recording protocol. Instead of relying on fixed-length sliding windows, AEMG identifies contraction events from energy variations and represents them as compact neuromuscular tokens, while ordered token groups describe the coordinated activity of multiple muscles during motion. A spatially and temporally conditioned Transformer is then used to encode these token sequences, preserving information about electrode position, activation timing, and sequential structure. For pre-training, the model constructs a discrete library of contraction prototypes through vector-quantized reconstruction and further learns contextual dependencies by recovering masked neuromuscular tokens from surrounding observations. Experiments under leave-one-subject-out and low-label adaptation settings show that the learned representation improves robustness to unseen users and reduces the amount of calibration data required for gesture recognition. These findings suggest that event-level token modeling offers a scalable route toward adaptable and data-efficient EMG-based motor-intent understanding.