Recognition and prediction of individual and combined muscular activation modes via surface EMG analysis

Abstract

The paper discusses how recognition of individual and combined muscular activation modes (functions) and the prediction of intended such modes can be accomplished by identifying parameters of noninvasive surface EMG signals. It outlines the mathematical analysis of surface EMG signal to facilitate such recognition and related prediction, including recognition of intention (in terms of attempts) to activate motor functions from the EMG, without accessing the CNS itself, in cases where a patient, say, a high-level amputee does not have the final-activation muscles and joints. The EMG activity thus allows to interpret and recognize CNS commands from minute variations in the parameters of surface EMG signals that record changes in the firing of motor neurons triggering contractions in related muscle fibers. We note that although in popular media this is sometimes referred to as detection of “thoughts”, no thoughts are detected, but only motor-outcomes of thoughts as found in the EMG signal. Examples of concrete cases where such recognition or prediction were accomplished in the author’s lab and in devices that came out of that lab, are given as are references to these in the literature over the last 35 years.

Keywords

• prediction, combined muscular activation modes, surface EMG