ICA for surface electromyogram

Surface electromyogram (SEMG) is an indicator of the underlying muscle activity and can be useful for human control interface. One difficulty in the use of SEMG for identifying complex movements is the mixing of muscle activity from other muscles, referred to cross-talk. Similarity in frequency and time domain makes the separation of muscle activity from different muscles extremely difficult. Independent Component Analysis (ICA) is a useful technique for blind source separation. This paper reports investigations to test the effectiveness of using ICA for such applications. It determines the impact of different conditions on the reliability of the separation. The paper reports the evaluation of issues related to the properties of the signals and number of sources. The paper also tests Zibulevsky's method of temporal plotting to identify number of independent sources in SEMG recordings. The results demonstrate that ICA is suitable for SEMG signals when the numbers of sources are not greater than the number of recordings. The inability of the system to identify the correct order and magnitude of the signals is also discussed. It is observed that even when muscle contraction is minimal, and signal is filtered using wavelets and band pass filters, Zibulevsky's sparse decomposition technique does not identify number of independent sources.