Over the last half-century, numerous attempts have been made to decompose surface EMG signals into individual motor unit firings. Unfortunately, identifying these firings amidst ambient noise and the activity of dozens of other motor units is inherently subject to errors, regardless of the method used.
In an effort to diminish such interference, Drs. Carlo J. De Luca and Joshua C. Kline have designed an innovative algorithm to reduce errors that occur during the sEMG decomposition process. The error reduction algorithm implements core concepts fundamental to estimation theory to obtain an improved estimate of motor unit firing times with fewer errors.
To test their design, they used the Delsys dEMG System for signal decomposition to realize the firing activity of 1,061 motor units from 36 voluntary isometric contractions.
Error Reduction yielded positive results: when applied to signals synthesized with physiological action potentials from motor units, errors were successfully reduced, improving the identification and location of firings in the decomposition result.
The improved estimates of motor unit firings will enhance analyses of precise firing statistics. Measures such as motor unit synchronization are now possible with a greater degree of accuracy than previously realized.
Kline JC and De Luca CJ. Error Reduction in EMG Signal Decomposition.
Journal of Neurophysiology
112: 2718-2728, 2014.