Surface electromyogram (sEMG) is a non-invasive recording and it has numerous applications. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent Component Analysis (ICA) provides the perfect answer of un-mixing a set of skin surface recordings into a vector (set) of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor set in sEMG recording. The purpose of this paper is to describe the use of source separation for sEMG based on ICA. We demonstrate how this can be used in practical sEMG experiments, when the number of recording channels for electrical muscle activities is varied. Keywords: Hand gesture sensing, Bio-signal analysis, Independent component analysis (ICA), Surface electromyography (sEMG), Blind source separation (BSS).
|Number of pages||14|
|Journal||International Journal of Innovative Computing, Information and Control|
|Publication status||Published - Mar 2011|