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

Xinyu Jiang,Metin Akay,Jiahao Fan,Xiangyu Liu,Xinming Ye,Chenyun Dai,Wei Chen,Edward A Clancy

doi:10.1109/tnsre.2021.3082551

Abstract

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.

Highlights

S URFACE electromyogram-based neural interface techniques [1] have attracted increasing attention in recent years
high-density S URFACE electromyogram (sEMG) (HD-sEMG) allows decomposition of the global multi-channel HD-sEMG at the macroscopic level into motor unit (MU) spike trains at the microscopic level [4], using independent component analysis (ICA) [5], [6]. This breakthrough analysis method to decode information of MUs from HD-sEMG has been applied in diverse fields such as neuromuscular physiology [7], clinical neurophysiology [8], neuromuscular biometrics [9] and neural interface [2]
Because we aimed to acquire HD-sEMG signals with multiple degree of freedom (DoF) to advance the development of multi-DoF prosthetic control, co-contractions of other fingers did not lead to the exclusion of trials

Summary

Introduction

S URFACE electromyogram (sEMG)-based neural interface techniques [1] have attracted increasing attention in recent years. HD-sEMG allows decomposition of the global multi-channel HD-sEMG at the macroscopic level into motor unit (MU) spike trains at the microscopic level [4], using independent component analysis (ICA) [5], [6]. This breakthrough analysis method to decode information of MUs from HD-sEMG has been applied in diverse fields such as neuromuscular physiology [7], clinical neurophysiology [8], neuromuscular biometrics [9] and neural interface [2]

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