Open Access Dataset and Toolbox of High-Density Surface Electromyogram Recordings

We provide an open access dataset of _High densitY Surface Electromyogram_ (HD-sEMG) _Recordings_ (named " _Hyser_ "). We acquired data from 20 subjects with each subject participating in our experiment twice on separate days following the same experiment paradigm. Our _Hyser_ dataset contains five sub- datasets: (1) pattern recognition (PR) dataset acquired during 34 hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Sub-dataset 1 can be used for gesture recognition studies. Sub- datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses.本研究提供一款命名为"Hyser"的开放获取高密度表面肌电图（High Density Surface Electromyogram, HD-sEMG）采集数据集。我们从20名受试者处采集实验数据，每名受试者于不同日期分别参与两次实验，且两次实验采用完全一致的范式。本Hyser数据集共包含5个子数据集：(1) 模式识别（Pattern Recognition, PR）数据集：采集自34种手部手势的执行过程；(2) 最大自主肌肉收缩（Maximal Voluntary Muscle Contraction, MVC）数据集：采集自受试者单独收缩每一根手指的过程；(3) 单自由度（One-Degree of Freedom, DoF）数据集：采集自每根手指进行变力收缩的过程；(4) 多自由度（N-Degree of Freedom, N-DoF）数据集：采集自多根手指组合的指定收缩任务过程；(5) 随机任务数据集：采集自无指定力轨迹的手指组合随机收缩过程。子数据集1可用于手势识别相关研究。子数据集2至5同时记录了单根手指的肌力数据，因此可用于神经假体比例控制领域的相关研究。

搜集汇总

背景与挑战

背景概述

该数据集是一个名为'Hyser'的高密度表面肌电图（HD-sEMG）开放访问数据集，包含20名受试者的两次实验数据，涵盖五个子数据集：手势识别、最大自主收缩、单指力变化、多指组合收缩和随机收缩。这些数据可用于手势识别和神经假体比例控制研究，特别适用于机器学习、生物医学工程和康复技术领域。