Negentropy analysis of surface electromyogram signal

This study deals with measuring the non-Gaussianity in surface electromyogram signal (sEMG). The signal was obtained from biceps brachii muscle during elbow flexion at four different levels of maximum voluntary contraction (MVC). Typically the sEMG generated from constant-force, constant angle, non-fatiguing contractions is modelled as a stochastic process, and its probability density function (pdf) is assumed to be Gaussian. Results of utilizing negentropy for characterizing the non-Gaussianity of sEMG signal indicate that its pdf is clearly non-Gaussian during light contractions (below 30% of MVC) and it tends to a Gaussian process at higher force levels. The results validate the application of higher order statistics (HOS) based methods in sEMG signal processing at low levels of MVC

[1]  Kenzo Akazawa,et al.  INDEPENDENT COMPONENT ANALYSIS AS PREPROCESSING TOOL FOR DECOMPOSITION OF SURFACE ELECTRODE-ARRAY ELECTROMYOGRAM , 2003 .

[2]  N. Hogan,et al.  Probability density of the surface electromyogram and its relation to amplitude detectors , 1999, IEEE Transactions on Biomedical Engineering.

[3]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[4]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[5]  Richard D. Deveaux,et al.  Applied Smoothing Techniques for Data Analysis , 1999, Technometrics.

[6]  Aapo Hyvärinen,et al.  New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit , 1997, NIPS.

[7]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[8]  K. Nazarpour,et al.  A Novel Feature Extraction Scheme for Myoelectric Signals Classification Using Higher Order Statistics , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[9]  I. W. Hunter,et al.  Estimation of the conduction velocity of muscle action potentials using phase and impulse response function techniques , 1987, Medical and Biological Engineering and Computing.

[10]  D. Gravel,et al.  Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. , 1997, Journal of Electromyography & Kinesiology.

[11]  Damjan Zazula,et al.  Decomposition of surface EMG signals using non-linear LMS optimisation of higher-order cumulants , 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).

[12]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .