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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Autors principals:	Imad Eddine Tibermacine, Samuele Russo, Francesco Citeroni, Giuseppe Mancini, Abdelaziz Rabehi, Amal H. Alharbi, El-Sayed M. El-kenawy, Christian Napoli
Format:	Artigo
Idioma:	Inglês
Publicat:	Frontiers Media S.A. 2025-05-01
Col·lecció:	Frontiers in Human Neuroscience
Matèries:	EEG denoising generative adversarial network Wasserstein GAN brain-computer interface deep learning
Accés en línia:	https://www.frontiersin.org/articles/10.3389/fnhum.2025.1583342/full
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