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

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 1035 - 1046
• Main Authors: Jiang, Xinyu, Liu, Xiangyu, Fan, Jiahao, Ye, Xinming, Dai, Chenyun, Clancy, Edward A., Akay, Metin, Chen, Wei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmark testing; Benchmarks; Computer applications; Datasets; Degrees of freedom; Density; Dextrous fingers; Electrodes; Electromyography; Finger; Fingers; Force; Forearm; Gesture recognition; hand gesture recognition; HD-sEMG; Implants; Independent component analysis; Indexes; Muscle contraction; Muscles; Muscular function; neural interface; Neural prostheses; Pattern recognition; Proportional control; prosthetic control; Prosthetics; Rehabilitation; Skeletal muscle; Task analysis; Wrist
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2021.3082551

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.