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Imaging the Behavior of Motor Units by Decomposition of the EMG Signal

This slide presentation was delivered as a tutorial lecture at the 2008 Annual Meeting of the American College of Sports Medicine on May 28 in Indianapolis, USA.

The presentation described a new technology for obtaining new parameters of the EMG signal which provide insight into the control of motor units during muscle contractions. Whereas the EMG signal detected from the muscle is almost exclusively used to relate the amplitude and timing of the muscle excitation to other characteristics of muscle contractions, the new parameters provide information appertaining to: the firing rate of motor units, the relationship of firing rates among the concurrently active motor units and the force produced by the muscle, synchronization of motor unit firings, among other parameters.

The approach consists of separating the EMG signal into the constituent motor unit action potential trains. This process is referred to as “decomposition”. We have been working on this concept for over three and one-half decades, overcoming numerous obstacles that required the development of our proprietary Artificial Intelligence framework. Two decades ago we developed a proof for assuring that the output of the decomposition is correct and accurate (Mambrito and De Luca, 1984, EEG and Clin. Neurophysiol.).

Most of our algorithms for decomposing the EMG signal were developed on indwelling EMG signals which were collected with a special quadrifilar sensor first reported in IEEE BME Trans. (De Luca and Forrest, 1972). It has since undergone revisions and is now in use by other research groups. We have achieved decomposition accuracies of 85%, with the remaining unresolved signal being decomposed with a sophisticated operator-assisted editor.  Our investigations with the indwelling EMG decomposition technology brought fourth new concepts in the control of motor units, such as the “Common Drive” and the “Onion Skin”.

More recently we have perfected a technology that is able to automatically decompose surface EMG signals into their constituent motor unit trains. The accuracy of the decomposition is in the neighborhood of 90% for signals collected during maximal force contractions. This technology represents a paradigm shift because it is non-invasive and may be used in non-clinical environments without the need for clinically trained personnel. It offers a new exploratory dimension for investigators working in motor control, sports science, movement science, behavioral science, as well as for clinicians.


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