Common spatial-spectral analysis of EMG signals for multiday and multiuser myoelectric interface

Published on Aug 1, 2019in Biomedical Signal Processing and Control2.94
· DOI :10.1016/j.bspc.2019.101572
Xinjun Sheng15
Estimated H-index: 15
(SJTU: Shanghai Jiao Tong University),
Bo Lv2
Estimated H-index: 2
(SJTU: Shanghai Jiao Tong University)
+ 1 AuthorsXiangyang Zhu21
Estimated H-index: 21
(SJTU: Shanghai Jiao Tong University)
Abstract Practical implementation of myoelectric interfaces have been largely hindered by cumbersome training and retraining procedures required for use across multiple days or for multiple users. We thus present a common spatial-spectral analysis (CSSA) framework to eliminate the need for retraining over multiple days or for multiple users. The CSSA is implemented through spectral decomposition and common modes analysis, maximumly utilizing the common spatial-spectral electromyography (EMG) mode from multiple days or for multiple users. Experiments involving two scenarios were conducted to simulate the application of multiday or multiuser myoelectric interface. Eight healthy and three amputee subjects participated in the first experiment for ten consecutive days, and seven healthy subjects participated in the second experiment involving a multiuser interface. Experimental results demonstrated that the control performance without retraining the myoelectric interface with the CSSA was significantly improved, and the classifier model pre-trained by background data under CSSA enabled EMG signals from new days or users to be recognized without training or retraining. The results could serve as a foundation for practical implementation of myoelectric interfaces.
  • References (44)
  • Citations (0)
#1Zhiyuan Lu (TIRR Memorial Hermann)H-Index: 2
#2Kai-yu Tong (HKU: University of Hong Kong)H-Index: 21
Last.Ping Zhou (University of Texas Health Science Center at Houston)H-Index: 10
view all 5 authors...
#1Asim Waris (AAU: Aalborg University)H-Index: 2
#2Imran Khan Niazi (New Zealand College of Chiropractic)H-Index: 13
Last.Ernest Nlandu Kamavuako ('KCL': King's College London)H-Index: 13
view all 8 authors...
#1Mahmoud Tavakoli (UC: University of Coimbra)H-Index: 12
#2Carlo Benussi (UC: University of Coimbra)H-Index: 1
Last.Joao Luis Lourenco (UC: University of Coimbra)H-Index: 1
view all 3 authors...
#1Dario Farina (Imperial College London)H-Index: 70
#2Ivan Vujaklija (Imperial College London)H-Index: 9
Last.Oskar C. Aszmann (Medical University of Vienna)H-Index: 24
view all 10 authors...
#1Giho Jang (UNLV: University of Nevada, Las Vegas)H-Index: 5
#2Junghoon Kim (Hanyang University)H-Index: 16
Last.Youngjin Choi (Hanyang University)H-Index: 24
view all 4 authors...
#1Jianwei Liu (SJTU: Shanghai Jiao Tong University)H-Index: 5
#2Xinjun Sheng (SJTU: Shanghai Jiao Tong University)H-Index: 15
Last.Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
view all 5 authors...
#1Dario Farina (GAU: University of Göttingen)H-Index: 70
#2Ales Holobar (University of Maribor)H-Index: 23
#1John A. Spanias (Rehabilitation Institute of Chicago)H-Index: 4
#2Eric J. Perreault (NU: Northwestern University)H-Index: 30
Last.Levi J. Hargrove (Rehabilitation Institute of Chicago)H-Index: 29
view all 3 authors...
Cited By0
View next paperCorrelation analysis of electromyogram signals for multiuser myoelectric interfaces.