High-Density Surface EMG Denoising Using Independent Vector Analysis

High-density surface electromyography (HD-sEMG) can provide rich temporal and spatial information about muscle activation. However, HD-sEMG signals are often contaminated by power line interference (PLI) and white Gaussian noise (WGN). In the literature, independent component analysis (ICA) and canonical correlation analysis (CCA), as two popular used blind source separation techniques, are widely used for noise removal from HD-sEMG signals. In this paper, a novel method to remove PLI and WGN was proposed based on independent vector analysis (IVA). Taking advantage of both ICA and CCA, this method exploits the higher order and second-order statistical information simultaneously. Our proposed method was applied to both simulated and experimental EMG data for performance evaluation, which was at least 37.50% better than ICA and CCA methods in terms of relative root mean squared error and 28.84% better than ICA and CCA methods according to signal to noise ratio. The results demonstrated that our proposed method performed significantly better than either ICA or CCA. Specifically, the mean signal to noise ratio increased considerably. Our proposed method is a promising tool for denoising HD-sEMG signals while leading to a minimal distortion.