文献类型：期刊文献

英文题名：Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings

作者：Jiang, Xinyu[1];Liu, Xiangyu[2];Fan, Jiahao[1];Ye, Xinming[3];Dai, Chenyun[1];Clancy, Edward A.[4];Akay, Metin[5];Chen, Wei[1]

机构：[1]Fudan Univ, Sch Informat Sci & Technol, Ctr Intelligent Med Elect, Shanghai 200433, Peoples R China;[2]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Sports Sci & Engn, Shanghai 200237, Peoples R China;[4]Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA;[5]Univ Houston, Dept Biomed Engn, Houston, TX 77204 USA

年份：2021

卷号：29

起止页码：1035

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20212310460208);WOS:【SCI-EXPANDED(收录号:WOS:000660632300001)】；

基金：This work was supported in part by the Shanghai Municipal Science and Technology Major Project under Grant 2017SHZDZX01, in part by the Shanghai Pujiang Program under Grant 19PJ1401100, and in part by the Natural Science Foundation of Shanghai under Grant 20ZR1403400.

语种：英文

外文关键词：Wrist; Electrodes; Task analysis; Muscles; Force; Indexes; Benchmark testing; HD-sEMG; neural interface; hand gesture recognition; prosthetic control

摘要：We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named "Hyser"), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used 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. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.