Decomposition strategy for surface EMG with few channels：a simulation study.

OBJECTIVE: In the specific use of electromyogram (EMG) driven prosthetics, the user's disability reduces the space available for the electrode array. We propose a framework for EMG decomposition adapted to the condition of a few channels (less than 30 observations), which can elevate the potential of prosthetics in terms of cost and applicability.

APPROACH: The new framework contains a peel-off approach, a refining strategy for motor unit spike train (MUST) and motor unit action potential (MUAP) and a re-subtracting strategy to adapt the framework to few channels environments. Simulated EMG signals were generated to test the framework. In addition, we quantify and analyze the effect of strategies used in the framework.

MAIN RESULTS: The results show that the new algorithm has an average improvement of 19.97% in the number of motor units (MUs) identified compared to the control algorithm. Quantitative analysis of the usage strategies shows that the re-subtracting and refining strategies can effectively improve the performance of the framework under the condition of few channels.

SIGNIFICANCE: These prove that the new framework can be applied to few channel conditions, providing a optimization space for neural interface design in cost and user adaptation.