A VMD-Based Noise-Specific and Adaptive Removal Method for High-Density Surface EMG

High-density surface electromyogram (HD-sEMG) undertakes the task of detecting the activity of motor units (MUs). However, its recordings are often mixed with power line interference and its harmonics (PLI), white Gaussian noise (WGN), baseline wandering (BW), and motion artifact (MA). The poor signal-to-noise ratio (SNR) is an obstacle for clinical applications. The surface electromyogram (sEMG) preprocessing technology rarely considers the diversity and specificity of noise, that is, the algorithm is only for a certain type of noise or only uses one method to deal with all kinds of noise. In this article, we propose a method based on variational mode decomposition (VMD) to specifically remove these four types of noise, which makes full use of the characteristics of noise. A variance-dependent adaptive parameter optimization method was proposed for PLI and WGN removal, and the higher-order statistics (HOS) were further used to adaptively suppress WGN without reference signals. The method was first evaluated using simulated data and then experimentally validated on three different databases involving not only healthy subjects but also amputees, specifically in comparison with previous methods. Both simulated and experimental results showed that the proposed method was significantly superior to the traditional methods (Wilcoxon signed-rank test, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} \lt 0.001$ </tex-math></inline-formula>). It resulted in 14.11%, 59.71%, and 5.76%, better than traditional methods on the three experimental databases, respectively. Our proposed method has positive implications for sEMG-based or HD-sEMG-based human-robot interfaces.