LAUSR

Many studies have demonstrated the feasibility and benefits of Conduction Velocity (CV) estimation from surface electromyograms (EMGs) in various experimental conditions. Among them, a method based on optical flow was proposed recently, demonstrating relatively accurate CV estimation for EMG signals acquired in monopolar mode. We extended this method by a new data model that compensates more realistically for the spatial Motor Unit Action Potential (MUAP) shape variability and enables accurate CV estimation also in single-differential acquisition mode. The proposed modification was validated on 5000 synthetic Motor Units (MUs) with known CV and direction of fibres. It was shown that, in the noiseless case, the mean CV estimation error was significantly lower for our proposed modification compared to the original CV estimation procedure by up to 2% in the case of monopolar EMG signals and by up to 18.6% for single-differential EMG signals. When estimating fibre directions, the mean error was lower by up to 2.4° (for monopolar EMG signals) and 9.6° (for single-differential EMG signals). The results of tests with 10dB and 20dB noise further demonstrated the robustness of the proposed algorithm to noise in MUAP estimation.