文献类型：期刊文献

英文题名：Quantifying Spatial Activation Patterns of Motor Units in Finger Extensor Muscles

作者：Jiang, Xinyu[1];Ren, Haoran[1];Xu, Ke[1];Ye, Xinming[2];Dai, Chenyun[1];Clancy, Edward A.[3];Zhang, Yuan-Ting[4];Chen, Wei[1]

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

年份：2021

卷号：25

期号：3

起止页码：647

外文期刊名：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

收录：;EI(收录号：20211110075430);WOS:【SCI-EXPANDED(收录号:WOS:000626521100004)】；

基金：This work was supported in part by the National Key R&D Program of China under Grant 2017YFE0112000, in part by the Shanghai Pujiang Program under Grant 19PJ1401100, and in part by the Shanghai Municipal Science and Technology Major Project, under Grant 2017SHZDZX01.

语种：英文

外文关键词：Muscles; Electrodes; Informatics; Indexes; Electromyography; Biomechanics; Shape; Finger movement mechanism; high-density sEMG decomposition; motor unit behavior

摘要：The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.