Machine learning-based estimation of EMG baseline noise standard deviation without rest trials

Accurate estimation of resting noise standard deviation (σ_noise) in surface electromyography (EMG) is essential for EMG amplitude estimation, particularly during low-level contractions where signal-to-noise ratios are low. Conventional σ_noise estimates are made from rest-state EMG recordings, but such data are not always available or routinely recorded in real-world settings. This study compared three methods of σ_noise estimation: 1) direct rest-state measurement ("truth"), and two methods intended to remove the need for explicit rest trials: 2) a novel machine learning (ML) approach utilizing active contractions, and 3) a fixed σ_noise value of 3 % maximum voluntary EMG (MVE). The ML model was trained on simulated EMG, then fine-tuned on EMG recordings from constant-force and force-varying elbow contraction of 62 subjects spanning three different EMG acquisition systems. Direct resting σ_noise measurements had a median absolute inter-trial difference of 0.06 % MVE. ML had a median absolute difference from rest-based σ_noise of 1.4 % MVE. The fixed σ_noise approach had a median absolute difference (1.71 % MVE) from rest-based σ_noise that did not differ significantly from the ML results. In a separate evaluation of baseline EMG noise reduction, omission of noise correction performed worst, ML and the fixed σ_noise value performed similarly but statistically better (45 % noise reduction compared to no noise correction), and noise calibration from a rest contraction performed statistically best (75 % noise reduction compared to no noise correction). These results suggest the feasibility of ML-or use of a fixed σ_noise value-as alternatives to direct EMG noise measurement, enabling more reliable EMG analysis in scenarios where rest-state data cannot be collected.