A comprehensive review of deep learning models for denoising EEG signals: challenges, advances, and future directions

Abstract Recent technological advancements have led to a significant increase in electroencephalogram (EEG)-based applications, ranging from clinical diagnosis and brain computer interfaces (BCI) to sleep studies and the monitoring of cognitive tasks. However, raw EEG signals are highly susceptible...

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Autores principales:	Vandana Akshath Raj, Tejasvi Parupudi, Ananthakrishna Thalengala, Subramanya G. Nayak
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Springer 2025-10-01
Colección:	Discover Applied Sciences
Materias:	Artifact removal Autoencoders Convolutional neural networks Electroencephalogram Emerging trends Generative adversarial networks
Acceso en línea:	https://doi.org/10.1007/s42452-025-07808-2
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