The identification of number of active muscles during a complex action is useful information to identify the action, and to determine pathologies. Biosignals such as surface electromyogram are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions results in difficulty in identifying the number of active sources from the multiple channel recordings. ICA has been applied to sEMG to separate the signals originating from different sources. But it is often difficult to determine the number of active sources that may vary between different actions and gestures. This paper reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The paper proposes the use of value of the determinant of the global matrix generated using sub-band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface.