title: Preferred sensor sites for surface EMG signal decomposition.
abstract: Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646-57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602-15). This advancement has widespread appeal among researchers and clinicians because of the ease of use, reduced risk of infection, and the greater number of motor unit action potential trains obtained compared to needle sensor techniques. In this study we investigated the influence of the sensor site on the number of identified motor unit action potential trains in six lower limb muscles and one upper limb muscle with the intent of locating preferred sensor sites that provided the greatest number of decomposed motor unit action potential trains, or motor unit yield. Sensor sites rendered varying motor unit yields throughout the surface of a muscle. The preferred sites were located between the center and the tendinous areas of the muscle. The motor unit yield was positively correlated with the signal-to-noise ratio of the detected sEMG. The signal-to-noise ratio was inversely related to the thickness of the tissue between the sensor and the muscle fibers. A signal-to-noise ratio of 3 was found to be the minimum required to obtain a reliable motor unit yield.
authors.affiliation: NeuroMuscular Research Center, Boston University, Boston, MA 02215, USA. farahz@bu.edu
authors.forename: Farah
authors.initials: F
authors.lastname: Zaheer
authors.forename: Serge H
authors.initials: SH
authors.lastname: Roy
authors.forename: Carlo J
authors.initials: CJ
authors.lastname: De Luca
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000293:Adolescent; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D009046:Motor Neurons / Q000502:physiology; D009119:Muscle Contraction / Q000502:physiology; D009132:Muscles / Q000294:innervation / Q000502:physiology; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D012885:Skinfold Thickness; D013499:Surface Properties; D055815:Young Adult