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

Nazarpour, Kianoush; Al-Timemy, Ali H.; Bugmann, Guido; Jackson, Andrew

doi:10.1016/j.brainresbull.2012.09.012

Cited by 94 publications

(74 citation statements)

References 25 publications

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“…The finding results of the present study are similar to the findings results of Nazarpour et al [9], in which the kurtosis is a suitable Gaussianity test and the mean bicoherence cannot be used as a Gaussianity test for sEMG signals. However, in this paper, negentropy is also a suitable Gaussianity testing technique and has a slightly higher correlation with external load and muscle force than kurtosis.…”

Section: G Analysis Of Methodssupporting

confidence: 90%

“…Several features have been originally designed to be used with non-Gaussian data, such as higher order statistics (HOS) and independent component analysis (ICA) [9]. The successful use of HOS or ICA depends on the reliability of the Gaussianity test [8].…”

Section: Discussionmentioning

confidence: 99%

“…The analysis of sEMG PDF using kurtosis and negentropy showed that the sEMG PDF depends on the strength of contraction. The PDF of sEMG signal tends to be more Gaussian when the strength of contraction increases [7][8][9][10]. Based on the analysis of mean bicoherence power, the EMG PDF is highly non-Gaussian at low-and high-strength of contractions and tends to be Gaussian at midstrength of contraction in Kaplanis et al [11] and the EMG PDF becomes less Gaussian as an increasing strength of contraction [12].…”

Section: Introductionmentioning

confidence: 95%

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Probability density of electromyography signal for different levels of contraction of biceps brachii

Thongpanja

Phinyomark

Limsakul

et al. 2013

2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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The probability density function (PDF) of surface electromyography (sEMG) signals can be modelled with the Gaussian and Laplacian PDFs. However, the sEMG PDF is dependent on the levels of contraction of the muscles. Different techniques have been proposed for testing Gaussianity levels of sEMG, i.e., kurtosis, negentropy, and mean bicoherence power, whereas the suitable technique has not been reported yet. In this paper, the experimental sEMG PDF and its relationship with the levels of muscle contraction were re-examined and the suitable Gaussianity test was presented based on sEMG acquired from biceps brachii muscle during the static contraction of the muscle at different load levels. The results show that the EMG PDF was non-Gaussian at low level of contraction and it tends to be more Gaussian at high level of contraction. Kurtosis and negentropy decreased as muscle load levels increased, whereas the consistent results between mean bicoherence power and muscle load levels were not found. Negentropy showed a better linear relationship with the levels of contraction of biceps brachii than other techniques.

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Section: G Analysis Of Methodssupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

Probability density of electromyography signal for different levels of contraction of biceps brachii

Thongpanja

Phinyomark

Limsakul

et al. 2013

2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

View full text Add to dashboard Cite

show abstract

“…It was found in [35] that the probability distribution of the surface EMG signal tends to be either super-Gaussian or Gaussian, depending on the contraction levels. Therefore for the simplicity, in this case, we treat the probability distribution of the state S to be Gaussian of the form:…”

Section: Extraction Of Human's Co-contraction Pattern Using Markov Chmentioning

confidence: 99%

Can a Soft Robotic Probe Use Stiffness Control Like a Human Finger to Improve Efficacy of Haptic Perception?

Sornkarn

Nanayakkara

2017

IEEE Trans. Haptics

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Abstract-When humans are asked to palpate a soft tissue to locate a hard nodule, they regulate the stiffness, speed, and force of the finger during examination. If we understand the relationship between these behavioral variables and haptic information gain (transfer entropy) during manual probing, we can improve the efficacy of soft robotic probes for soft tissue palpation, such as in tumor localization in minimally invasive surgery. Here, we recorded the muscle co-contraction activity of the finger using EMG sensors to address the question as to whether joint stiffness control during manual palpation plays an important role in the haptic information gain. To address this question, we used a soft robotic probe with a controllable stiffness joint and a force sensor mounted at the base to represent the function of the tendon in a biological finger. Then, we trained a Markov chain using muscle co-contraction patterns of human subjects, and used it to control the stiffness of the soft robotic probe in the same soft tissue palpation task. The soft robotic experiments showed that haptic information gain about the depth of the hard nodule can be maximized by varying the internal stiffness of the soft probe.

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“…Also, modeling studies examined 10 whether optimal motor control can be implemented by modular control schemes (Nori 11 and Frezza, 2005;Chhabra and Jacobs, 2006;Berniker et al, 2009;Neptune et al, 2009;12 Alessandro et al, 2013a). Finally, studies of human motor behavior investigated the 13 robustness of modules by imposing alterations on muscle coordination of healthy indi-14 viduals (de Rugy et al, 2012(de Rugy et al, , 2013Nazarpour et al, 2012a;Steele et al, 2015) and testing 15 muscle activations in clinical populations (Gizzi et al, 2011;Clark et al, 2010;Cheung 16 et al, 2012;Roh et al, 2013).…”

Section: Critical Evaluation Of Modular Motor Controlmentioning

confidence: 99%

Space-by-time modular decomposition effectively describes whole-body muscle activity during upright reaching in various directions

Hilt

Delis

Pozzo

et al. 2017

Preprint

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The modular control hypothesis suggests that motor commands are built 1 from precoded modules whose specific combined recruitment can allow the 2 performance of virtually any motor task. Despite considerable experimental 3 support, this hypothesis remains tentative as classical findings of reduced di-4 mensionality in muscle activity may also result from other constraints (biome-5 chanical couplings, data averaging or low dimensionality of motor tasks). 6Here we assessed the effectiveness of modularity in describing muscle activity 7 in a comprehensive experiment comprising 72 distinct point-to-point whole-

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

Cited by 94 publications

References 25 publications

Probability density of electromyography signal for different levels of contraction of biceps brachii

Probability density of electromyography signal for different levels of contraction of biceps brachii

Can a Soft Robotic Probe Use Stiffness Control Like a Human Finger to Improve Efficacy of Haptic Perception?

Space-by-time modular decomposition effectively describes whole-body muscle activity during upright reaching in various directions

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