 Our proposed method is a wavelet-based algorithm designed to automatically detect and remove EEG artifacts in single-channel recordings. It is able to adaptively attenuate artifacts of different natures, including ocular, muscular, and movement artifacts. This makes it suitable for use in real-time applications, such as monitoring during surgery or anesthesia. We tested the performance of our method on publicly available datasets and found that it outperformed other methods in terms of both accuracy and robustness. Furthermore, we applied our method to monitor general anesthesia and found that it was successful in removing artifacts without compromising the quality of the EEG signal. This article was authored by Mateo Dora and David Holkman.