The extraction of neural strategies from the surface EMG: 2004-2024.

This review follows two previous papers [Farina et al. 96: 1486-1495, 2004; Farina et al. 117: 1215-1230, 2014] in which we reflected on the use of surface electromyography (EMG) in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modeling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of nonmeasurable parameters by inverse modeling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the nonstationarities in dynamic contractions, and to decompose signals in real time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 yr since our first review, we conclude that the recording and analysis of surface EMG signals have seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.