title: A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees.
abstract: BACKGROUND
  Most of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses.
  
  
  METHODS
  Four transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method.
  
  
  RESULTS
  The classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel EEG) were obtained with classification accuracies of 84.2 and 87.0%, respectively, which were about 7.2 and 10% higher than the accuracy by using only 32-channel sEMG input.
  
  
  CONCLUSIONS
  This study demonstrated the feasibility of fusing sEMG and EEG signals towards improving motion classification accuracy for above-elbow amputees, which might enhance the control performances of multifunctional myoelectric prostheses in clinical application.
  
  
  BACKGROUND
  The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.
authors.affiliation: Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Xiangxin
authors.initials: X
authors.lastname: Li
authors.affiliation: Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Oluwarotimi Williams
authors.initials: OW
authors.lastname: Samuel
authors.affiliation: Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China.|Department of Biology, South University of Science and Technology of China, Shenzhen, 518055, China.
authors.forename: Xu
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.affiliation: Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China. peng.fang@siat.ac.cn.
authors.forename: Peng
authors.initials: P
authors.lastname: Fang
authors.affiliation: Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China. gl.li@siat.ac.cn.
authors.forename: Guanglin
authors.initials: G
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000671:Amputation, Surgical; D000674:Amputees; D001186:Artificial Limbs; D016002:Discriminant Analysis; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008297:Male; D009038:Motion; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D014955:Wrist Joint
keywords: Amputee; EEG; Hybrid interface; Motion classification; Multifunctional prosthesis; Pattern recognition; Rehabilitation; Signal Fusion; sEMG