Testing of instantaneity hypothesis for blind source separation of extensor indicis and extensor digiti minimi surface electromyograms

Human muscle activity can be assessed with surface electromyography (SEMG). Depending on electrode location and size, the recording volume under the sensor is likely to measure electrical potentials emanating from muscles other than the muscle of interest. This crosstalk issue makes interpretation of SEMG data difficult. The purpose of this paper was to study a crosstalk reduction technique called blind source separation (BSS). Most straightforward separation techniques rely on linearity and instantaneity (LI) of signal mixtures on the sensors. Literature on BSS for SEMG often makes hypothesis of linearity and instantaneity of the mixing model. Using simulation of SEMG mixtures and real SEMG recordings on the human extensor indicis (EI) and extensor digiti minimi (EDM) muscles during a task consisting of selective successive activations of EI and EDM muscles, cross-correlation between the sensors was proven to be directly dependent on instantaneity of the sources. Instantaneity hypothesis testing on real SEMG recordings showed that source instantaneity hypothesis is very sensitive to electrode location along the fibers direction. Source separation gains using JADE BSS algorithm depend strongly on instantaneity hypothesis. Using LI BSS on SEMG requires great attention to electrode positioning; we provide a tool to test these on EI/EDM muscles.