Pre-processing of Multi-channel sEMG Signals Based on ICA and Spectral Curve Descriptors
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In this paper, a set of multi-channel sEMG was given to examine the coordination between quadriceps andhamstring. To extract valuable muscle information and also describe muscles? activities from noisy multi-channel sEMG, advanced signal processing techniques are required to cancel additive noise, remove artifacts and reduce crosstalkes among electrodes. In the last two decades, Independent Component Analysis (ICA) techniques have been successfully used to remove crosstalkes among electrodes. Besides that, ICA are multi-channel adaptive signal processing techniques and do not require any prior information of the signal.In our study, ICA is used to eliminate the crosstalks among the electrodes. To improve the performance of our system, spectral descriptors were also used to describe muscle activities. Many experiments and various simulations have been conducted. Our experimental results showed that ICA can remove the crosstalks and further improve the signal quality while three differentspectral descriptors which are energy, MPF and VMF can work together to describe a muscle activity effectively.
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