Surface electromyogram (EMG) has a relatively large detection volume, so that it could include contributions both from the target muscle of interest and from nearby regions (i.e., crosstalk). This interference can prevent a correct interpretation of the activity of the target muscle, limiting the use of surface EMG in many fields. To counteract the problem, selective spatial filters have been proposed, but they reduce the representativeness of the data from the target muscle. A better solution would be to discard only crosstalk from the signal recorded in monopolar configuration (thus, keeping most information on the target muscle). An inverse modelling approach is here proposed to estimate the contributions of different muscles, in order to focus on the one of interest. The method is tested with simulated monopolar EMGs from superficial nearby muscles contracted at different force levels (either including or not model perturbations and noise), showing statistically significant improvements in information extraction from the data. The median over the entire dataset of the mean squared error in representing the EMG of the muscle under the detection electrode was reduced from 11.2% to 4.4% of the signal energy (5.3% if noisy data were processed); the median bias in conduction velocity estimation (from 3 monopolar channels aligned to the muscle fibres) was decreased from 2.12 to 0.72 m/s (1.1 m/s if noisy data were processed); the median absolute error in the estimation of median frequency was reduced from 1.02 to 0.67 Hz in noise free conditions and from 1.52 to 1.45 Hz considering noisy data.

表面肌电图 (EMG) 具有相对较大的检测量，因此它可以包括来自感兴趣的目标肌肉和附近区域（即串扰）的贡献。这种干扰会妨碍对目标肌肉活动的正确解释，从而限制了表面肌电图在许多领域的使用。为了解决这个问题，已经提出了选择性空间滤波器，但它们降低了来自目标肌肉的数据的代表性。更好的解决方案是仅丢弃以单极配置记录的信号中的串扰（因此，将大部分信息保留在目标肌肉上）。这里提出了一种逆向建模方法来估计不同肌肉的贡献，以关注感兴趣的肌肉。该方法使用模拟单极 EMG 来自在不同力水平（包括或不包括模型扰动和噪声）收缩的浅表附近肌肉进行测试，显示从数据中提取信息的统计显着改进。在表示检测电极下肌肉的 EMG 时，整个数据集的均方误差的中值从信号能量的 11.2% 减少到 4.4%（如果处理噪声数据，则为 5.3%）；传导速度估计的中值偏差（从与肌肉纤维对齐的 3 个单极通道）从 2.12 降低到 0.72 m/s（如果处理噪声数据，则为 1.1 m/s）；在无噪声条件下，估计中值频率的中值绝对误差从 1.02 赫兹减少到 0.67 赫兹，考虑到噪声数据，从 1.52 赫兹减少到 1.45 赫兹。