An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

Electroencephalogram (EEG) recordings often contain artifacts that would lower signal quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most of them rely on visual inspection and manual operations, which is time/labor-consuming, subjective, and incompatible to filter massive EEG data in real-time. In this paper, we proposed a deep learning framework named Ar…