Deep Learning-Based Techniques for Electroencephalogram (EEG) Signal Denoising

Electroencephalography (EEG) signals denoising is crucial for neural signal interpretation, particularly in complex noise conditions. Traditional methods often fail to address these conditions effectively. In contrast, deep learning-based techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Generative Adversarial Networks (GANs), and Transformers offer enhanced adaptability and robustness by tuning to diverse noise characteristics. This paper presents a systematic review and comparative analysis of updated EEG denoising models, utilizing the EEGdenoiseNet benchmark dataset for a consistent evaluation framework. Performance of each model is assessed using the Relative Root Mean Square Error (RRMSE) and Correlation Coefficient (CC) across time domains. The study aims to elucidate the strengths of each denoising model, providing insights for model selection. Furthermore, it outlines each model's practical implications and limitations, setting the stage for future innovations in EEG signal processing.