HD-sEMG-CORE: An Open-Source Hybrid Network Algorithm for Efficient Compression and Accurate Restoration of High-Density Surface Electromyography Signals

High-density surface electromyography (HD-sEMG) provides distinct advantages over traditional bipolar sEMG, including improved spatial resolution and enhanced localization of muscle activity. However, the collection of HD-sEMG signals requires the use of multiple electrodes over a small skin area, resulting in large data volumes. Managing such data necessitates substantial storage capacity, high bandwidth, and effective handling of information redundancy to ensure both usability and accuracy. Furthermore, challenges such as electrode contact variability and external electromagnetic interference often compromise signal quality, which calls for robust signal restoration techniques. This paper presents HD-sEMG-CORE, an open-source hybrid network algorithm designed for efficient compression and accurate restoration of HD-sEMG signals. The proposed approach combines generative adversarial networks (GANs) and variational autoencoders (VAEs) for signal compression, while a U-Net-based convolutional neural network (CNN) is employed to restore the features of corrupted or noisy signals in the latent space, completing the reconstruction process. Performance metrics, including mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (ρ), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), demonstrate the effectiveness of HD-sEMG-CORE in both compression and restoration tasks. This methodology offers an efficient and precise solution for managing large HD-sEMG datasets, with potential applications in neurophysiology and neuroengineering.