dEMG Technology for Extracting Information from the EMG Signal

dEMG Technology for Extracting Information from the EMG Signal

The underlying neural mechanisms that regulate how muscles generate and control force are embedded in the characteristics of electrical pulses that originate in the brain and are transmitted to the muscle fibers by nerves. These pulses, known as motor unit action potentials (MUAPTs), can be detected on the surface of a muscle, but only after they superimpose into a complex signal known as the EMG signal. If it were possible to extract the individual MUAPTs, the information contained in their firing behavior would enable us to understand the underlying neural mechanisms and how they are impaired by injury and neuromuscular diseases.

For the past eight decades, the individual MUAPTs could only be detected with invasive and painful methods that yielded only a few examples of the rich electrical activity within the muscle, providing limited and unreliable data.

We have been developing a dEMG technology that has been commercialized by our industrial partner Delsys (see dEMG website) to provide a non-invasive, high-yield, and fully automated means of extracting the MUAPs from the detected EMG signal. The technology uses a small specially designed array sensor and a proprietary algorithm that decomposes the surface EMG signal into the individual MUAPTs. The advancement represents an important breakthrough that surpasses attempts by other competitive groups. A discussion on the advantages of our dEMG technology may be found in Delsys Blog Article.

The applications of this technology are stunning. Imagine designing studies that reveal the control schemes used by the Central Nervous System to generate and control force production. Do the control schemes alter during fatigue? Imagine identifying modifications in the firing characteristics of muscle fibers in diseased and dysfunctional muscles – in stroke, in Parkinson, in ALS, etc. Imagine using the information content in the firing behavior of muscle fibers to provide precise control of artificial prostheses.

For a demonstration of how to use our dEMG technology refer to Delsys Webinar and see the surprising firing behavior of muscle fibers during fatigue.

The dEMG technology has been described extensively in the peer-reviewed literature. Independent researchers from other institution have verified its accuracy. However, it is constrained to work on EMG signals obtained during isometric (constant length) muscle contractions. An evolving version is revealing promising results on dynamic cyclic muscle contractions. See Publication list below.

Applications
  • Ageing Studies
  • Muscle Fatigue
  • Muscle Force Modelling
  • Motor Control
  • Prosthetics & Orthotics

Publications

Hu X, Rymer WZ, Suresh NL, Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle. Journal of Neural Engineering, 11(2), 2014. PMID: 24556614.

Hu X, Rymer WZ, Suresh NL. Assessment of validity of A High-Yield Surface Electromyogram Decomposition. J Neuroeng Rehabil 23;10(1):99. 2013. PMID: 24059856.

Hu X, Rymer WZ, Suresh NL. Motor Unit Pool Organization Examined via Spike Triggered Averaging of the Surface Electromyogram. J Neurophysiol 110(5):1205-20. 2013. PMID: 23699053.

Hu X, Rymer WZ, Suresh NL. Reliability of spike triggered averaging of the surface electromyogram for motor unit action potential estimation. Muscle Nerve 48(4): 557-70, 2013. PMID: 23424086.

De Luca CJ, Nawab SH, Kline JC. Clarification of methods used to validate surface EMG decomposition algorithms as described by Farina et al. (2014). J Appl Physiol 118(8), 2015. PMID: 25878218.

De Luca CJ, Chang SS, Roy SH, Kline JC, and Nawab SH. Decomposition of Surface EMG Signals from Cyclic Dynamic Contractions. J Neurophysiol 113(6):1941-51, Mar 2015. PMID: 25540220. 

Kline JC and De Luca CJ. Error Reduction in EMG Signal Decomposition. Journal of Neurophysiology. 112(11):2718-28, 2013. PMID: 25210159.

De Luca CJ, Roy S, Chang SS. Tracking Motor Unit Firings during Dynamic Cyclic Contractions. The 7th World Congress of Biomechanics, Boston, July 2014.

De Luca CJ and Nawab SH. Reply to Farina and Enoka: The Reconstruct-and-Test Approach Is the Most Appropriate Validation for Surface EMG Signal Decomposition to Date. Journal of Neurophysiology, 105: 983-984, 2011. 

Nawab SH, Chang SS, and De Luca CJ. High-yield decomposition of surface EMG signals. Clinical Neurophysiology, 121(10):1602-1615, 2010.PMID: 20430694.

Nawab SH, Chang SS, and De Luca CJ. Surface EMG Signal Decomposition Using Empirically Sustainable BioSignal Separation Principles. The 31st Annual International Conference of the IEEE EMBS, Minneapolis, September 2009. PMID: 19964658.

Chang SS, De Luca CJ, and Nawab SH. Aliasing Rejection in Precision Decomposition of EMG Signals. The 30th Annual International Conference of the IEEE EMBS, Vancouver, August 2008. PMID: 19163833.

De Luca CJ, Adam A, Wotiz R, Gilmore LD, and Nawab SH. Decomposition of surface EMG signals. Journal of Neurophysiology, 96: 1646-1657, 2006.

Contessa P, Puleo A, De Luca CJ. Is the notion of central fatigue based on a solid foundation? J Neurophysiol February 2016 115:967-977. PMID: 26655823

De Luca CJ, Kline JC. The common input notion, conceived and sustained by conjecture. J Neurophysiol February 2016 115:1079-1080. PMID: 26905085 

Herda TJ, Miller JD, Trevino MA, Mosier EM, Gallagher PM, Fry AC, Vardiman JP. The change in motor unit firing rates at derecruitment relative to recruitment is correlated with type I myosin heavy chain isoform content of the vastus lateralis in vivo. Acta Physiol (Oxf) Oct. 2015. PMID: 26513624.

Kline JC, De Luca CJ. Synchronization of Motor Unit Firings: An Epiphenomenon of Firing Rate Characteristics Not Common Inputs. J Neurophysiol. Oct 2015. PMID: 26490288.

Ye X, Beck TW, Wages NP. Influence of prolonged static stretching on motor unit firing properties. Muscle Nerve. Sept 2015. PMID: 26378724.

Hu X, Suresh AK, Rymer WZ, Suresh NL. Assessing altered motor unit recruitment patterns in paretic muscles of stroke survivors using surface electromyography. J Neural Eng. 12(6). Sept. 2015 PMID: 26402920.

McManus L, Hu X, Rymer WZ, Lowery MM, Suresh NL. Changes in motor unit behavior following isometric fatigue of the first dorsal interosseous muscle. J Neurophysiol 113(9):3186-96. May, 2015. PMID: 25761952.

De Luca CJ, Contessa P. Biomechanical Benefits of the Onion-skin Motor Unit Control Scheme. Journal of Biomechanics, 48(2), Jan 2015. PMID: 25527890.

Stock MS, Thompson BJ. Effects of Barbell Deadlift Training on Submaximal Motor Unit Firing Rates for the Vastus Lateralis and Rectus Femoris. PLoS ONE 9(12), Dec 2014. PMID: 25531294.

Hu X, Rymer WZ, Suresh NL. Control of motor unit firing during step-like increases in voluntary force. Front. Hum. Neurosci. 8:721, 2014. PMID: 25309395.

Trevino MA, Herda TJ, Cooper MA. The effects of poliomyelitis on motor unit behavior during repetitive muscle actions: a case report. BMC Research Notes 2014, 7:611. PMID: 25194883. 

De Luca CJ and Kline JC. Statistically rigorous calculations do not support Common Input and Long-Term synchronization of motor unit firings. Journal of Neurophysiology. 112(11). Dec 2014. PMID: 25210152.

De Luca CJ, Kline JC, Contessa P. Transposed firing activation of motor units. J Neurophysiol, 12:962-970, Aug, 2014. PMID: 24899671.
 
Defreitas JM, Beck TW, Ye X, Stock MS. Synchronization of low- and high-threshold motor units. Muscle Nerve Jul 28, 2013. PMID: 23893653.

Zaheer F, Roy SH, De Luca CJ. Preferred Sensor Sites for Surface EMG Signal Detection. Physiological Measurement, 33(2), 2012. PMID: 22260842.

De Luca CJ and Contessa P. Hierarchical Control of Motor Units in Voluntary Contractions. Journal of Neurophysiology, 107(1): 178-195, 2012. PMID: 21975447.

De Luca CJ and Kline JC. Influence of proprioceptive feedback on the firing rate and recruitment of motorneurons. Journal of Neural Engineering, 9(1):016007, 2012. PMID: 22183300. 

Stock WS, Beck TW, Defreitas JM. Effects of Fatigue on Motor Unit Firing Rate versus Recruitment Threshold Relationship. Muscle & Nerve, 45: 100-109, 2012. PMID: 22190315.

Beck TW, Kasishke PR 2nd, Stock MS, Defreitas JM. Eccentric exercise does not affect common drive in the biceps brachii. Muscle & Nerve, 46: 759-66, 2012. PMID: 22941727.

Beck TW, Stock MS, Defreitas JM. Effects of fatigue on intermuscular common drive to the quadriceps femoris. Int J Neurosci 122(10): 574-82, 2012. PMID: 22591395.
 
Hu X, Suresh AK, Li X, Rymer WZ, Suresh NL. Impaired motor unit control in paretic muscle post stroke assessed using surface electromyography: a preliminary report. Conf Proc IEEE Eng Med Biol Soc 2012: 4116-9, 2012. PMID: 23366833.

Suresh N, Li X, Zhou P, Rymer WZ. Examination of Motor Unit Control Properties in Stroke Survivors Using Surface EMG Decomposition: A Preliminary Report. The 33rd Annual International Conference of the IEEE EMBS, Boston, September 2011. PMID: 22256256. 

Richards J, Selfe J. EMG Decomposition of Vastus Medialis and Vastus Lateralis in normal subjects and patellofemoral patients: A new way of assessing the balance of muscle function? International Patellofemoral Research Retreat, Ghent 31 August – September 2011. 

Beck TW, DeFreitas JM, Stock MS, Dillon MA. Effects of resistance training on force steadiness and common drive. Muscle & Nerve, 43(2) 245-250, 2011. PMID: 21254090.

Beck TW, DeFreitas JM, Stock MS. The Effects of a Resistance Training Program on Average Motor Unit Firing Rates. Clinical Kinesiology, 65(1), 2011. 

De Luca CJ, Hostage EC. Relationship between firing rate and recruitment threshold of motoneurons in voluntary isometric contractions. Journal of Neurophysiology, 104: 1034-1046, 2010. PMID: 20554838.


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