Deep learning models offer significant potential over traditional linear techniques for denoising EEG signals, though challenges in generalizability and interpretability remain.
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 to artifacts, which hinder accurate analysis and interpretation of brain signals. Traditional artifact removal techniques often fall short owing to their linear assumptions and limited generalizability. Deep learning (DL) based approaches have shown remarkable potential for capturing nonlinear and complex features of EEG signals. This review provides a comprehensive overview of state-of-the-art deep learning models developed for EEG denoising, highlighting their architectural designs, strengths, limitations, and performance analysis. In addition, the study emphasizes the importance of accurate evaluation frameworks and benchmarking, as well as the challenges of generalizability and interpretability. Future research directions include the integration of hybrid architectures, self-supervised learning, and real-time implementation. This article serves as a resource for researchers aiming to advance EEG denoising through modern deep learning approaches.
Raj et al. (Wed,) studied this question.