IEEE Trans Neural Syst Rehabil Eng. 2019 May;27(5):887-894. doi: 10.1109/TNSRE.2019.2910387. Epub 2019 Apr 11.
High-density surface electromyography (HD-EMG) provides detailed information about muscle activation. However, HD-EMG recordings can be interfered by motion artifacts and power line noise. In this paper, an interference detection and removal method with minimal distortion of the EMG was developed based on the independent component analysis (ICA). After the source separation, the independent components with power line noise were detected based on the spectra and were processed with notch filters. Components with motion artifacts were identified by analyzing the peak frequency of the spectrum, and motion artifacts were filtered with a high-pass filter and an amplitude thresholding method. The EMG signals were then reconstructed based on the processed source signals. The denoising performance was evaluated on both simulated and experimental EMG signals. The results showed that our method was significantly better than the digital filter method and the conventional ICA-based method where components with interferences were set to zero. Namely, our method showed a minimal distortion of the denoised EMG amplitude and frequency and a higher yield of decomposed motor units. Our interference detection and removal algorithm can be used as an effective preprocessing procedure and can benefit macro level EMG analysis and micro level motor unit analysis.
高密度表面肌电图 (HD-EMG) 可提供有关肌肉活动的详细信息。然而，HD-EMG 记录可能会受到运动伪影和电源线噪声的干扰。本文基于独立成分分析 (ICA) 开发了一种具有最小肌电图失真的干扰检测和去除方法。在源分离后，根据频谱检测到带有电源线噪声的独立分量，并使用陷波滤波器进行处理。通过分析频谱的峰值频率来识别带有运动伪影的分量，并使用高通滤波器和幅度阈值方法过滤运动伪影。然后根据处理后的源信号重建肌电图信号。在模拟和实验肌电图信号上评估了去噪性能。结果表明，与数字滤波器方法和传统的基于 ICA 的方法（其中将带有干扰的分量设置为零）相比，我们的方法显著更好。也就是说，我们的方法显示出对去噪肌电图幅度和频率的最小失真，以及更高的分解运动单元产量。我们的干扰检测和去除算法可用作有效的预处理过程，并有助于宏观水平的肌电图分析和微观水平的运动单元分析。
