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

TL;DR

This paper introduces AEMG, a self-supervised token modeling approach for EMG signals that enhances robustness and reduces calibration needs in gesture recognition across diverse conditions.

Contribution

It proposes a novel event-level token representation and a Transformer-based encoding method for more adaptable EMG-based motor intent inference.

Findings

Improved robustness to unseen users and sessions.

Reduced calibration data needed for accurate gesture recognition.

Demonstrated effectiveness across multiple public datasets.

Abstract

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.