Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches

Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches

IntroductionElectroencephalography (EEG) signals frequently contain substantial noise and interference, which can obscure clinically and scientifically relevant features. Traditional denoising approaches, such as linear filtering or wavelet thresholding, often struggle with nonlinear or time-varying...

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Bibliographic Details
Main Authors: Imad Eddine Tibermacine, Samuele Russo, Francesco Citeroni, Giuseppe Mancini, Abdelaziz Rabehi, Amal H. Alharbi, El-Sayed M. El-kenawy, Christian Napoli
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Human Neuroscience
Subjects:
EEG denoising
generative adversarial network
Wasserstein GAN
brain-computer interface
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2025.1583342/full
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https://www.frontiersin.org/articles/10.3389/fnhum.2025.1583342/full

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