Interference removal from electromyography based on independent component analysis

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.