A note on the probability distribution function of the surface electromyogram signal☆

Highlights ► We recorded surface EMG signals with a biofeedback setup at 7 different contraction levels. ► We estimated the PDF, kurtosis and bicoherence index of the measured signals. ► We show that the EMG PDF at low contraction levels is super-Gaussian. ► At higher contraction forces, the EMG PDF tends to a Gaussian distribution.

[1]  D. Gravel,et al.  Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. , 1997, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[2]  Carlo J. De Luca,et al.  Physiology and Mathematics of Myoelectric Signals , 1979 .

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

[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]  C. D. De Luca.  Physiology and Mathematics of Myoelectric Signals , 1979, IEEE Transactions on Biomedical Engineering.

[6]  P. Herberts.  The Control of upper-extremity prostheses and orthoses , 1974 .

[7]  G. Hefftner,et al.  The electromyogram (EMG) as a control signal for functional neuromuscular stimulation. II. Practical demonstration of the EMG signature discrimination system , 1988, IEEE Transactions on Biomedical Engineering.

[8]  S Micera,et al.  Control of Hand Prostheses Using Peripheral Information , 2010, IEEE Reviews in Biomedical Engineering.

[9]  T. Sanger,et al.  Prolonged Electromyogram Biofeedback Improves Upper Extremity Function in Children With Cerebral Palsy , 2010, Journal of child neurology.

[10]  Mamun Bin Ibne Reaz,et al.  Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction , 2009, Expert Syst. J. Knowl. Eng..

[11]  Terence D Sanger,et al.  Bayesian filtering of myoelectric signals. , 2007, Journal of neurophysiology.

[12]  Adrian D. C. Chan,et al.  A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses , 2005, IEEE Transactions on Biomedical Engineering.

[13]  R. Merletti,et al.  Modeling of surface myoelectric signals--Part II: Model-based signal interpretation. , 1999, IEEE transactions on bio-medical engineering.

[14]  A. Jackson,et al.  Flexible Cortical Control of Task-Specific Muscle Synergies , 2012, The Journal of Neuroscience.

[15]  Serge H. Roy,et al.  Modeling of surface myoelectric signals. II. Model-based signal interpretation , 1999, IEEE Transactions on Biomedical Engineering.

[16]  K. Nazarpour,et al.  Surface EMG Signal Classification Using a Selective Mix of Higher Order Statistics , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[17]  Ahmad R. Sharafat,et al.  Application of Higher Order Statistics to Surface Electromyogram Signal Classification , 2007, IEEE Transactions on Biomedical Engineering.

[18]  K. Nazarpour,et al.  Negentropy analysis of surface electromyogram signal , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[19]  D. Winter,et al.  Models of recruitment and rate coding organization in motor-unit pools. , 1993, Journal of neurophysiology.

[20]  S. Arjunan,et al.  Kurtosis and negentropy investigation of myo electric signals during different MVCs , 2011, ISSNIP Biosignals and Biorobotics Conference 2011.

[21]  P.A. Parker,et al.  Signal processing for the multistate myoelectric channel , 1977, Proceedings of the IEEE.

[22]  Andrew Jackson,et al.  Learning a Novel Myoelectric-Controlled Interface Task , 2008, Journal of neurophysiology.

[23]  L. Ince,et al.  Experimental foundations of EMG biofeedback with the upper extremity: A review of the literature , 1984, Biofeedback and Self-Regulation.

[24]  L. Hadjileontiadis,et al.  Bispectral analysis of surface EMG , 2000, 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099).

[25]  M. Hinich.  Testing for Gaussianity and Linearity of a Stationary Time Series. , 1982 .

[26]  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.

[27]  Robert W. Mann,et al.  Myoelectric Signal Processing: Optimal Estimation Applied to Electromyography - Part I: Derivation of the Optimal Myoprocessor , 1980, IEEE Transactions on Biomedical Engineering.

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

[29]  Euljoon Park,et al.  Adaptive filtering of the electromyographic signal for prosthetic control and force estimation , 1995, IEEE Transactions on Biomedical Engineering.