PMID- 35303735
OWN - NLM
STAT- MEDLINE
DCOM- 20220406
LR  - 20220605
IS  - 1741-2552 (Electronic)
IS  - 1741-2552 (Linking)
VI  - 19
IP  - 2
DP  - 2022 Apr 5
TI  - Improved online decomposition of non-stationary electromyogram via signal 
      enhancement using a neuron resonance model: a simulation study.
LID - 10.1088/1741-2552/ac5f1b [doi]
AB  - Objective. Motor unit (MU) discharge information obtained via the online 
      electromyogram (EMG) decomposition has shown promising prospects in multiple 
      applications. However, the nonstationarity of EMG signals caused by the rotation 
      (recruitment-derecruitment) of MUs and the variation of MU action potentials 
      (MUAP) can significantly degrade the online decomposition performance. This study 
      aimed to develop an independent component analysis-based online decomposition 
      method that can accommodate the nonstationarity of EMG signals.Approach. The EMG 
      nonstationarity can make the separation vectors obtained beforehand inaccurate, 
      resulting in the reduced amplitudes of the peaks corresponding to firing events 
      in the source signal (independent component) and then the decreased accuracy of 
      firing events. Therefore, we utilized the FitzHugh-Nagumo (FHN) resonance model 
      to enhance the firing peaks in the source signal in order to improve the 
      decomposition accuracy. A two-session approach was used with the offline session 
      to extract the separation vectors and train the FHN models. In the online 
      session, the source signal was estimated and further processed using the FHN 
      model before detecting the firing events in a real-time manner. The proposed 
      method was tested on simulated EMG signals, in which MU rotation and MUAP 
      variation were involved to mimic the nonstationarity of EMG recordings.Main 
      results. Compared with the conventional method, the proposed method can improve 
      the decomposition accuracy significantly (88.70% +/- 4.17% vs. 92.43% +/- 2.79%) by 
      enhancing the firing peaks, and more importantly, the improvement was more 
      prominent when the EMG signal had stronger background noises (87.00% +/- 3.70% vs. 
      91.66% +/- 2.63%).Conclusions. Our results demonstrated the effectiveness of the 
      proposed method to utilize the FHN model to improve the online decomposition 
      performance on the nonstationary EMG signals. Further development of our method 
      has the potential to improve the performance of the neural decoding system that 
      utilizes the MU discharge information and promote its application in the 
      neural-machine interface.
CI  - (c) 2022 IOP Publishing Ltd.
FAU - Zheng, Yang
AU  - Zheng Y
AUID- ORCID: 0000-0001-9118-8333
AD  - Institute of Engineering & Medicine Interdisciplinary Studies, School of 
      Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's 
      Republic of China.
AD  - State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong 
      University, Xi'an 710049, People's Republic of China.
FAU - Xu, Guanghua
AU  - Xu G
AUID- ORCID: 0000-0002-1294-4741
AD  - Institute of Engineering & Medicine Interdisciplinary Studies, School of 
      Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's 
      Republic of China.
AD  - State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong 
      University, Xi'an 710049, People's Republic of China.
FAU - Li, Yixin
AU  - Li Y
AD  - Institute of Engineering & Medicine Interdisciplinary Studies, School of 
      Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's 
      Republic of China.
FAU - Qiang, Wei
AU  - Qiang W
AD  - Institute of Engineering & Medicine Interdisciplinary Studies, School of 
      Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's 
      Republic of China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220405
PL  - England
TA  - J Neural Eng
JT  - Journal of neural engineering
JID - 101217933
SB  - IM
MH  - Action Potentials/physiology
MH  - *Algorithms
MH  - Computer Simulation
MH  - Electromyography/methods
MH  - *Motor Neurons/physiology
MH  - Muscle, Skeletal/physiology
MH  - Signal Processing, Computer-Assisted
OTO - NOTNLM
OT  - EMG decomposition
OT  - FitzHugh-Nagumo model
OT  - independent component analysis
OT  - online signal processing
EDAT- 2022/03/19 06:00
MHDA- 2022/04/07 06:00
CRDT- 2022/03/18 20:13
PHST- 2021/12/10 00:00 [received]
PHST- 2022/03/18 00:00 [accepted]
PHST- 2022/03/19 06:00 [pubmed]
PHST- 2022/04/07 06:00 [medline]
PHST- 2022/03/18 20:13 [entrez]
AID - 10.1088/1741-2552/ac5f1b [doi]
PST - epublish
SO  - J Neural Eng. 2022 Apr 5;19(2). doi: 10.1088/1741-2552/ac5f1b.