Towards Cross-Subject EMG Pattern Recognition via Dual-Branch Adversarial Feature Disentanglement

TL;DR

This paper introduces a dual-branch adversarial neural network that disentangles EMG features to enable calibration-free cross-subject pattern recognition and biometric identification, improving robustness and practicality.

Contribution

It proposes a novel end-to-end model that separates pattern-specific and subject-specific features for better cross-user generalization in EMG recognition.

Findings

Outperforms baseline methods in cross-subject scenarios

Achieves robust recognition on unseen users

Enables task-invariant biometric identification

Abstract

Cross-subject electromyography (EMG) pattern recognition faces significant challenges due to inter-subject variability in muscle anatomy, electrode placement, and signal characteristics. Traditional methods rely on subject-specific calibration data to adapt models to new users, an approach that is both time-consuming and impractical for large-scale, real-world deployment. This paper presents an approach to eliminate calibration requirements through feature disentanglement, enabling effective cross-subject generalization. We propose an end-to-end dual-branch adversarial neural network that simultaneously performs pattern recognition and individual identification by disentangling EMG features into pattern-specific and subject-specific components. The pattern-specific components facilitate robust pattern recognition for new users without model calibration, while the subject-specific components enable downstream applications such as task-invariant biometric identification. Experimental results demonstrate that the proposed model achieves robust performance on data from unseen users, outperforming various baseline methods in cross-subject scenarios. Overall, this study offers a new perspective for cross-subject EMG pattern recognition without model calibration and highlights the proposed model's potential for broader applications, such as task-independent biometric systems.