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.The surface electromyography(sEMG) signal emanates when people contract their muscles. The sEMG signal contains plenty of information about muscle activity. Therefore,it can be used in activity recognition, which makes great contribution to medical devices, e.g., protheses or orthoses control systems. Here,a de-noising technique is presented which applies singular spectrum analysis(SSA) to de-noise sEMG signals. The principle of SSA is to decompose the original time series into a set of additive time series in which noise can be easily distinguished from the useful signal. Unlike transform-based algorithms, such as discrete wavelet transform, SSA is a time-series analysis algorithm which is completely driven by signal itself. This data-driven nature makes SSA very useful for sEMG signal de-noising.

A SSA-based de-noising technique for surface electromyography signals