Feature and Window Optimization for SNR Estimation in Power-Line-Contaminated Facial EMG Signals
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Abstract
Electromyography (EMG) signals are widely used in biomedical and rehabilitation applications; however, they frequently suffer from power-line interference (PLI), which compromises signal quality and impacts subsequent analysis. Therefore, it is important to be able to accurately estimate a signal-to-noise ratio (SNR) to assess data quality and guide the method of noise removal. This study investigates the effects of feature number and window size on the accuracy of SNR estimation in the EMG signal contaminated by the PLI signal. The EMG signal is contaminated with synthetic 50 Hz interference at controlled noise levels. Eight features are derived from windows of differing lengths, and the SNR was estimated utilizing 23 regression-based models. Results indicate that waveform activity (WA) and kurtosis (KURT) were found to be the best for estimating SNR. Using window sizes between 250 ms and 2000 ms, these features produced RMSE values from 3.95 to 3.17, demonstrating that larger windows enhance estimation accuracy. In addition, the application of the proposed model is preliminarily validated with real-world facial EMG signals.
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