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

From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG arXiv:2605.03462v3 Announce Type: replace-cross 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 reusa