The spinal circuitries combine the information flow from the supraspinal centers with the afferent input to generate the neural codes that drive the human skeletal muscles. The muscles transform the neural drive they receive from alpha motor neurons into motor unit action potentials (electrical activity) and force. Thus, the output of the spinal cord circuitries can be examined noninvasively by measuring the electrical activity of skeletal muscles at the surface of the skin i.e. the surface electromyogram (EMG). The recorded multi-muscle EMG activity pattern is generated by mixing processes of neural sources that need to be identified from the recorded signals themselves, with minimal or no a priori information available. Recently, multichannel source separation techniques that rely minimally on a priori knowledge of the mixing process have been developed and successfully applied to surface EMG. They act at different scales of information extraction to identify: (a) the activation signals shared by synergistic skeletal muscles, (b) the specific neural activation of individual muscles, separating it from that of nearby muscles i.e. from crosstalk, and (c) the spike trains of the active motor neurons. This review discusses the assumptions made by these methods, the challenges and limitations, as well as examples of their current applications.