Deep-learning based electroencephalogram denoising: a literature review

Electroencephalography (EEG) is a pivotal tool for exploring brain functions. However, the low amplitude of EEG signals renders them inherently susceptible to contamination from diverse physiological and environmental artifacts, including electromyogram artifacts, electrocardiogram interference, and electrical noise from power lines. These contaminants significantly hinder the analysis and interpretation of EEG data, posing substantial challenges for signal processing. Recently, deep learning paradigms have catalyzed significant progress in EEG denoising, with many studies reporting competitive reconstruction fidelity and artifact suppression in benchmarked settings. Despite this progress, there remains a notable gap in the literature regarding comprehensive reviews of deep learning-based EEG denoising strategies. To bridge this gap, we use the end-to-end denoising pipeline as an analytical framework, examining how data/target construction, input representation, modular architecture, objective design, and evaluation strategies influence model assumptions, the interpretation of model performance, and practical utility. We further discuss selective and multi-task denoising strategies, downstream validation, and model deployment as key issues for translating reconstruction performance into usable EEG applications. Finally, we identify future research directions aimed at developing more reliable, interpretable, and practically useful EEG denoising systems, thereby enhancing the utility of EEG technologies in broader applications.