TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement
<|reference_start|>TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement: Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remove sEMG contaminants, as non-invasive measurements render sEMG susceptible to various contaminants. However, these approaches often rely on heuristic-based optimization and are sensitive to the contaminant type. A more potent, robust, and generalized sEMG denoising approach should be developed for various healthcare and human-computer interaction applications. This paper proposes a novel neural network (NN)-based sEMG denoising method called TrustEMG-Net. It leverages the potent nonlinear mapping capability and data-driven nature of NNs. TrustEMG-Net adopts a denoising autoencoder structure by combining U-Net with a Transformer encoder using a representation-masking approach. The proposed approach is evaluated using the Ninapro sEMG database with five common contamination types and signal-to-noise ratio (SNR) conditions. Compared with existing sEMG denoising methods, TrustEMG-Net achieves exceptional performance across the five evaluation metrics, exhibiting a minimum improvement of 20%. Its superiority is consistent under various conditions, including SNRs ranging from -14 to 2 dB and five contaminant types. An ablation study further proves that the design of TrustEMG-Net contributes to its optimality, providing high-quality sEMG and serving as an effective, robust, and generalized denoising solution for sEMG applications.<|reference_end|>
@article{wang2024trustemg-net:,
    title={TrustEMG-Net: Using Representation-Masking Transformer with U-Net for
  Surface Electromyography Enhancement},
    author={Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao},
    journal={arXiv preprint arXiv:2410.03843},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03843},
    primaryClass={eess.SP cs.LG}
}