Advanced deep learning techniques for EEG artifact removal: a comprehensive review and future directions

This comprehensive review attempts to provide a complete overview of the many kinds of artifacts affecting electroencephalogram (EEG) signals with an emphasis on brain–computer interface (BCI) applications. The review involved a thorough search of different datasets, like PubMed, IEEE Explore, and Google Scholar. Papers covering EEG artifacts and related removal techniques that are published in the past 10 years served as the basis for the inclusion criteria. This review evaluates the EEG artifact reduction methods, with emphasis on external and internal artifacts affecting BCI. Some metrics, like RMSE, SNR, MSE, MAE, and RE, are considered to evaluate these. In neuroscience, adequate preprocessing is crucial for accurate data analysis, which ultimately improves clinical interpretation of EEGs. This comprehensive review highlights the relevance of eliminating artifacts from EEG signals to enhance the data quality for neuroscience applications, especially BCI.