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

Lookup NU author(s): Professor Kianoush Nazarpour, Professor Andrew Jackson

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Abstract

The probability density function (PDF) of the surface electromyogram (EMG) signals has been modelled with Gaussian and Laplacian distribution functions. However, a general consensus upon the PDF of the EMG signals is yet to be reached, because not only are there several biological factors that can influence this distribution function, but also different analysis techniques can lead to contradicting results. Here, we recorded the EMG signal at different isometric muscle contraction levels and characterised the probability distribution of the surface EMG signal with two statistical measures: bicoherence and kurtosis. Bicoherence analysis did not help to infer the PDF of measured EMG signals. In contrast, with kurtosis analysis we demonstrated that the EMG PDF at isometric, non-fatiguing, low contraction levels is super-Gaussian. Moreover, kurtosis analysis showed that as the contraction force increases the surface EMG PDF tends to a Gaussian distribution. (C) 2012 Elsevier Inc. All rights reserved.

Publication metadata

Author(s): Nazarpour K, Al-Timemy AH, Bugmann G, Jackson A

Publication type: Article

Publication status: Published

Journal: Brain Research Bulletin

Year: 2013

Volume: 90

Pages: 88-91

Print publication date: 06/10/2012

Date deposited: 11/11/2013

ISSN (print): 0361-9230

ISSN (electronic): 1873-2747

Publisher: Elsevier

URL: http://dx.doi.org/10.1016/j.brainresbull.2012.09.012

DOI: 10.1016/j.brainresbull.2012.09.012

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