Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi-masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. The results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal.

从附近的运动单元中去除伪影是处理扫描 EMG 记录时的主要目标之一。中值滤波或屏蔽最小二乘平滑 (MLSS) 等方法可用于消除记录中的伪影，每个位置仅释放一次运动单位电位 (MUP)。但是，如果记录每个位置的多次放电，则可以实现更有效的伪影去除。在这种情况下，处理通常涉及对每个位置可用的放电进行平均，然后在空间维度上应用中值滤波器。这种方法的主要缺点是中值滤波器往往会使信号波形失真。在本文中，我们提出了一种在空间维度上同时对多个放电进行操作的新算法。我们将此算法称为多掩蔽最小二乘平滑 (MMLSS) 算法：MLSS 算法在多次放电情况下的扩展。在不同的记录条件下，即在不同的肌肉收缩水平和每个位置的不同放电次数下，使用模拟的扫描 EMG 信号对该算法进行了测试。结果表明，该算法比任何以前可用的方法更有效地消除了伪影，并且不会扭曲信号的波形。