Wang Kun, Chen Xun, Wu Le, Zhang Xu, Chen Xiang, Wang Z Jane
IEEE Trans Neural Syst Rehabil Eng. 2020 Jun;28(6):1271-1281. doi: 10.1109/TNSRE.2020.2987709. Epub 2020 Apr 13.
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.
高密度表面肌电图（HD-sEMG）能够提供有关肌肉激活的丰富时间和空间信息。然而，HD-sEMG信号常常受到电源线干扰（PLI）和白高斯噪声（WGN）的污染。在文献中，独立成分分析（ICA）和典型相关分析（CCA）作为两种常用的盲源分离技术，被广泛用于去除HD-sEMG信号中的噪声。本文提出了一种基于独立向量分析（IVA）去除PLI和WGN的新方法。该方法利用ICA和CCA两者的优势，同时利用高阶和二阶统计信息。我们提出的方法应用于模拟和实验肌电图数据进行性能评估，在相对均方根误差方面比ICA和CCA方法至少好37.50%，根据信噪比比ICA和CCA方法好28.84%。结果表明，我们提出的方法比ICA或CCA的性能都显著更好。具体而言，平均信噪比有显著提高。我们提出的方法是一种很有前景的工具，用于对HD-sEMG信号进行去噪，同时使失真最小。
