Wavelet quantile normalization (WQN) is a nonparametric algorithm designed to remove transient artifacts from single-channel EEG in real-time. EEG monitoring machines suspend their output when artifacts in the signal are detected. Removing unpredictable EEG artifacts can improve the continuity of monitoring. We analyse here the WQN algorithm which consists in transporting wavelet coefficient distributions of an artifacted epoch into a reference, uncontaminated signal distribution. We show that the algorithm regularizes the signal. To confirm that the algorithm is well suited, we study the empirical distributions of the EEG and the artifacts wavelet coefficients. We compare the WQN algorithm to the classical wavelet thresholding methods and study their effect on the distribution of the wavelet coefficients: we report that the WQN algorithm preserves the distribution while the thresholding methods cause alterations. Finally, we show how the spectrogram computed from an EEG signal during clinical monitoring is cleaned by the WQN algorithm.

小波分位数归一化 (WQN) 是一种非参数算法，旨在实时去除单通道 EEG 中的瞬态伪影。当检测到信号中的伪影时，EEG 监测机器会暂停其输出。去除不可预测的脑电图伪影可以提高监测的连续性。我们在此分析 WQN 算法，该算法包括将人为时期的小波系数分布传输到参考的、未受污染的信号分布中。我们证明了该算法对信号进行了正则化。为了确认该算法非常适合，我们研究了 EEG 的经验分布和伪影小波系数。我们将 WQN 算法与经典的小波阈值方法进行比较，并研究它们对小波系数分布的影响：我们报告说，WQN 算法保留了分布，而阈值方法会导致改变。最后，我们展示了如何通过 WQN 算法清理在临床监测期间从 EEG 信号计算的频谱图。