From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG
Zhenghao Huang, Huilin Yao, Kaikai Wang
备注：
After further verification, we identified issues in the current version that may affect the reliability and reproducibility of the reported experimental results. In particular, part of the evaluation relies on a dataset for which the public-release/redistribution status and supporting validation remain unresolved
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.
ST-Topo GAN: A Motor EEG-to-EMG Decoding Model Matched to Wrist Movement Complexity
Zhengzhou University（郑州大学）；HumVerse (Zhengzhou) Technologies Co., Ltd.（HumVerse（郑州）科技有限公司）；Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology（河南省脑科学与脑机接口技术重点实验室）；Xi’an Jiaotong University（西安交通大学）；University of Electronic Science and Technology of China（电子科技大学）；Chinese Academy of Medical Sciences（中国医学科学院）
Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait
Universiti Kebangsaan Malaysia（马来西亚国民大学）；Qatar University（卡塔尔大学）；Dr Ram Manohar Lohia Avadh University（拉姆·马诺哈尔·洛希亚阿瓦德大学）；Xiamen University Malaysia（厦门大学马来西亚分校）；National Research and Innovation Agency（国家研究与创新署）
