Reconstruction of incomplete surface electromyography based on an adversarial autoencoder network

Yongxiang Zou, Long Cheng, Lijun Han

Abstract: Highlights•This paper proposed a new model, named SGMD-AAE, which provides a universal method to solve various signal missing problems caused by different reasons, such as poor communication, electrodes disconnection, and downsampling.•Experimental validation has been performed to assess the reconstructed sEMG signal performance. The outcomes indicate that the classification accuracy can exceed 84%, even when up to 90% of the sEMG signals are lost.•The experimental result indicates that the proposed SGMD-AAE model can reconstruct incomplete sEMG signals effectively, enabling the use of fewer electrodes in myoelectric control. This tool reduces computing resource consumption and is especially useful in resource-constrained scenarios.