Effects of long-term myoelectric signals on pattern recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8102 LNAI, Issue PART 1, 2013, Pages 396-404

  He, Jiayuan ^a   Zhang, Dingguo ^a   Sheng, Xinjun ^a   Zhu, Xiangyang ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

electromyography; long term signals; nonstationarity; pattern recognition; prosthesis

Indexed keywords

ELECTROMYOGRAPHY; MYOELECTRICALLY CONTROLLED PROSTHETICS; PATTERN RECOGNITION; PROSTHETICS; ROBOTICS;

FEATURE SPACE; MYOELECTRIC SIGNALS; NON-STATIONARITIES; PRACTICAL USE; PROSTHESIS CONTROL; QUANTIFICATION METHODS; RECOGNITION ACCURACY; TIME AND FREQUENCY DOMAINS;

BIOMEDICAL SIGNAL PROCESSING;

EID: 84884872487     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40852-6_40     Document Type: Conference Paper

References (14)

1. Hargrove, L.J., Scheme, E.J., Englehart, K.B., Hudgins, B.S.: Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18(1), 49-57 (2010)
2. Hudgins, B., Parker, P., Scott, R.: A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 40(1), 82-94 (1993)
3. Chen, X., Zhu, X., Zhang, D.: Use of the discriminant fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification. Physiological Measurement 30(12), 1399 (2009)
4. Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 50(7), 848-854 (2003)
5. Kang, W., Shiu, J., Cheng, C., Lai, J., Tsao, H., Kuo, T.: The application of cepstral coefficients and maximum likelihood method in emg pattern recognition[movements classification]. IEEE Transactions on Biomedical Engineering 42(8), 777-785 (1995)
6. Liu, J., Zhang, D., He, J., Zhu, X.: Effect of dynamic change of arm position on myoelectric pattern recognition. In: IEEE Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics (2012)
7. Hargrove, L., Englehart, K., Hudgins, B.: A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomedical Signal Processing and Control 3(2), 175-180 (2008)
8. He, J., Zhang, D., Zhu, X.: Adaptive pattern recognition of myoelectric signal towards practical multifunctional prosthesis control. In: Su, C.-Y., Rakheja, S., Liu, H. (eds.) ICIRA 2012, Part I. LNCS, vol. 7506, pp. 518-525. Springer, Heidelberg (2012)
9. Chen, X., Zhu, X., Zhang, D.: A discriminant bispectrum feature for surface electromyogram signal classification. Medical Engineering & Physics 32(2), 126-135 (2010)
10. He, J., Zhang, D., Sheng, X., Zhu, X.: Improved discrete fourier transform based spectral feature for surface electromyogram signal classification. In: 2013 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC (accepted, 2013)
11. Graupe, D., Salahi, J., Kohn, K.: Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals. Journal of Biomedical Engineering 4(1), 17-22 (1982)
12. Hargrove, L., Englehart, K., Hudgins, B.: A comparison of surface and intramuscular myoelectric signal classification. IEEE Transactions on Biomedical Engineering 54(5), 847-853 (2007)
13. Bunderson, N., Kuiken, T.: Quantification of feature space changes with experience during electromyogram pattern recognition control. IEEE Transactions on Neural Systems and Rehabilitation Engineering 20(3), 239-246 (2012)
14. De Luca, C.J.: Physiology and mathematics of myoelectric signals. IEEE Transactions on Biomedical Engineering (6), 313-325 (1979)