I. Introduction
Blind SOURCE separation (BSS) consists of recovering a set of signals (sources) of which only mixtures are available (observations). Neither the structure of the mixtures nor the source signals are known. The aim is to identify and decouple the mixtures [4]. Surface electromyographic (EMG) signals are the resultant of the electrical activity of the muscle fibers active during a muscle contraction. When two or more muscles close to each other are concomitantly active, the EMG signals detected over the skin are mixtures of contributions generated by all the active muscles. Indeed, the electric potential distribution generated by an intracellular action potential covers a large region over the skin due to the blurring effect (basically a low pass filtering) of the tissues separating the sources (the muscle fibers) and the recording electrodes [20]. The detection system has poor spatial selectivity, and thus it is often impossible to distinguish, from the interference EMG signal detected from a group of closely located muscles, the activity of the individual muscles. Separation of these activities is important in many applications, such as the control of prostheses [18], [19], the assessment of muscle coordination [24], or the reduction of crosstalk [9].