The WQN algorithm to adaptively correct artifacts in the EEG signal

TL;DR

The paper introduces the WQN algorithm, a nonparametric wavelet-based method for real-time correction of transient artifacts in single-channel EEG signals, aiming to improve continuous clinical monitoring.

Contribution

It presents the WQN algorithm that adaptively corrects EEG artifacts by transporting wavelet coefficient distributions, maintaining signal integrity better than classical thresholding methods.

Findings

WQN effectively regularizes EEG signals by preserving wavelet coefficient distributions.

Compared to thresholding, WQN better maintains the original signal's distribution.

WQN can be applied to clean spectrograms of EEG signals in real-time.

Abstract

Wavelet quantile normalization (WQN) is a nonparametric algorithm designed to efficiently remove transient artifacts from single-channel EEG in real-time clinical monitoring. Today, EEG monitoring machines suspend their output when artifacts in the signal are detected. Removing unpredictable EEG artifacts would thus allow to improve the continuity of the monitoring. We analyze 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 show that the WQN algorithm preserves the distribution while the thresholding methods can cause alterations. Finally, we show how the spectrogram computed from an EEG signal can be cleaned using the WQN algorithm.