A discriminant bispectrum feature for surface electromyogram signal classification

Medical Engineering and Physics

Volumn 32, Issue 2, 2010, Pages 126-135

  Chen, Xinpu ^a   Zhu, Xiangyang ^a   Zhang, Dingguo ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Classification; Discriminant bispectrum feature; Prosthetic control; sEMG signal

Indexed keywords

AUTOREGRESSIVE COEFFICIENT; BISPECTRUM; CLASSIFICATION; CLASSIFICATION ACCURACY; CLUSTERING INDEX; DISCRIMINANT BISPECTRUM FEATURE; FEATURE DISTRIBUTION; FEATURE EXTRACTION METHODS; FISHER LINEAR DISCRIMINANTS; MATRIX; MOTION PATTERN; NONLINEAR CLASSIFIERS; POWER SPECTRAL; QUANTITATIVE MEASUREMENT; ROOT MEAN SQUARE; SIGNAL CLASSIFICATION; SURFACE ELECTROMYOGRAM; TIME DOMAIN;

PROSTHETICS;

FEATURE EXTRACTION;

ACCURACY; ARTICLE; AUTOREGRESSIVE COEFFICIENT; CLASSIFICATION; CLUSTER ANALYSIS; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; DISCRIMINANT BISPECTRUM FEATURE; ELECTROMYOGRAM; ELECTROMYOGRAPHY; FEMALE; FISHER LINEAR DISCRIMINATION; HAND; HAND MOVEMENT; HUMAN; HUMAN EXPERIMENT; LINEAR SYSTEM; MALE; MATHEMATICAL MODEL; MYOELECTRIC CONTROL; POWER SPECTRAL DISTRIBUTION; PRIORITY JOURNAL; PROSTHESIS; ROOT MEAN SQUARE; STATISTICAL ANALYSIS; TASK PERFORMANCE; TIME DOMAIN STATISTICS; WRIST;

DISCRIMINANT ANALYSIS; ELECTROMYOGRAPHY; FEMALE; HAND; HUMANS; MALE; MOVEMENT; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED; WRIST;

EID: 75749089551     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2009.10.016     Document Type: Article

References (36)

1. Vodovnik L, Kreifeldt J, Caldwell R, Green L, Silgalis E, Craig P. Some topics on myoelectric control of orthotic/prosthetic systems, Rep EDC 4-67.
2. Merletti R., and Parker P. Electromyography: physiology, engineering, and noninvasive applications (2004), Wiley-IEEE Press
3. Hudgins B., Parker P., and Scott R. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 40 1 (1993) 82-94
4. Englehart K., and Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 50 7 (2003) 848-854
5. Englehart K., Hudgins B., Parker P., and Stevenson M. Classification of the myoelectric signal using time-frequency based representations. Medical Engineering and Physics 21 6-7 (1999) 431-438
6. Oskoei M, Hu H. Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Transactions on Biomedical Engineering 2008;55(8).
7. Shenoy P., Miller K., Crawford B., and Rao R. Online electromyographic control of a robotic prosthesis. IEEE Transactions on Biomedical Engineering 55 3 (2008) 1128-1135
8. Huang Y., Englehart K., Hudgins B., and Chan A. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Transactions on Biomedical Engineering 52 11 (2005) 1801-1811
9. Hargrove L., Englehart K., and Hudgins B. A comparison of surface and intramuscular myoelectric signal classification. IEEE Transactions on Biomedical Engineering 54 5 (2007) 847-853
10. Chiou Y., Luh J., Chen S., Lai J., and Kuo T. The comparison of electromyographic pattern classifications with active and passive electrodes. Medical Engineering and Physics 26 7 (2004) 605-610
11. Hu X., and Nenov V. Multivariate AR modeling of electromyography for the classification of upper arm movements. Clinical Neurophysiology 115 6 (2004) 1276-1287
12. Peleg D., Braiman E., Yom-Tov E., and Inbar G. Classification of finger activation for use in a robotic prosthesis arm. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10 4 (2002) 290-293
13. Micera S., Sabatini A., Dario P., and Rossi B. A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. Medical Engineering and Physics 21 5 (1999) 303-311
14. De Luca C. Physiology and mathematics of myoelectric signals. IEEE Transactions on Biomedical Engineering (1979) 313-325
15. Zazula D. Experience with surface EMG decomposition using higher-order cumulants. Signal Processing (2001) 19-24
16. Bilodeau M., Cincera M., Arsenault A., and Gravel D. Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. Journal of Electromyography and Kinesiology 7 2 (1997) 87-96
17. Akgul T., Sun M., Sclahassi R., and Cetin A. Characterization of sleep spindles using higher order statisticsand spectra. IEEE Transactions on Biomedical Engineering 47 8 (2000) 997-1009
18. Shahid S., Walker J., Lyons G., Byrne C., and Nene A. Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential. IEEE Transactions on Biomedical Engineering 52 7 (2005) 1195-1209
19. Khadra L., Al-Fahoum A., and Binajjaj S. A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques. IEEE Transactions on Biomedical Engineering 52 11 (2005) 1840-1845
20. Nikias C., and Raghuveer M. Bispectrum estimation: a digital signal processing framework. Proceedings of the IEEE 75 7 (1987) 869-891
21. Nikias C., and Petropulu A. Higher-order spectra analysis: a nonlinear signal processing framework (1993), Prentice-Hall
22. Nikias C., and Mendel J. Signal processing with higher-order spectra. IEEE Signal Processing Magazine 10 3 (1993) 10-37
23. Chandran V, Elgar S. Pattern recognition using invariants defined from higher order spectra-one dimensional inputs. IEEE Transactions on Signal Processing 1993;41(1).
24. Duda R., Hart P., and Stork D. Pattern classification and scene analysis (1973), Wiley, New York
25. Peter N., Joao P., and David J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence (1997) 711-720
26. Webb A. Statistical pattern recognition (1999), A Hodder Arnold Publication
27. Vapnik V. The nature of statistical learning theory (2000), Springer
28. Bishpo C. S.O. service, Pattern recognition and machine learning (2006), Springer
29. Duin R. PRTools version 3.0, a matlab toolbox for pattern recognition, pattern recognition group (2006), Delft University, Netherlands
30. Chang C, Lin C. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
31. Chen X, Zhu X. A novel spectral analysis method of EMG feature extraction for wrist motions recognition. In: Proceedings of the 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, 2008. BioRob; 2008. p. 73-8.
32. Zardoshti-Kermani M., Wheeler B., Badie K., and Hashemi R. EMG feature evaluation for movement control of upper extremityprostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering 3 4 (1995) 324-333
33. Zar J. Biostatistical analysis (1999), Prentice-Hall, Inc., Upper Saddle River (NJ, USA)
34. Makhoul J. Linear prediction: a tutorial review. Proceedings of the IEEE 63 4 (1975) 561-580
35. Fukuda O., Tsuji T., Kaneko M., and Otsuka A. A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Transactions on Robotics and Automation 19 2 (2003) 210-222
36. Jonathon B.A.L., Sensinger W., and Kuiken T.A. Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering 17 3 (2009) 270-278