A novel algorithm to remove electrical cross-talk between surface EMG recordings and its application to the measurement of short-term synchronisation in humans

Pairs of discharges of single motor units recorded in the same or different muscles often show synchronisation above chance levels. If large numbers of units are synchronous within and between muscles then the synchrony will be measurable in population recordings such as surface EMG. Measuring synchrony between surface EMG recordings has a number of practical and scientific advantages compared with single motor units recorded from intramuscular electrodes. However, the measurement of such synchrony in the time domain between surface EMGs is complicated because the recordings are contaminated by electrical cross-talk. In this study we recorded surface EMG simultaneously from five hand and forearm muscles during a precision grip task. Using a novel 'blind signal separation' algorithm, we were able to remove electrical cross-talk. The crosstalk-corrected EMGs could then be used to assess task-dependent modulation in both oscillatory (15-30 Hz) and non-oscillatory synchrony (all other frequencies). In agreement with previous studies, the oscillatory component was maximal during steady holding but abolished during movement. By contrast, the non-oscillatory component of the EiMG synchrony appeared remarkably constant throughout all phases of the task. We conclude that surface EMG recordings can be of considerable use in the assessment of population synchrony changes, providing that electrical cross-talk between nearby channels is removed using a statistical signal processing technique. Our results show a striking difference in the task-dependent modulation of oscillatory and non-oscillatory synchrony between muscles during a dynamic precision grip task.