EEG Based Deep

EEG-based deep learning focuses on using deep neural networks to analyze electroencephalogram (EEG) data, aiming to improve the accuracy and efficiency of brain-computer interfaces and other neurotechnology applications. Current research emphasizes improving model performance through advanced pre-processing techniques, exploring self-supervised learning to address data labeling challenges, and developing methods for enhancing model interpretability and generalizability across individuals. These advancements are significant for advancing our understanding of brain function and enabling more reliable and robust applications in healthcare, neurorehabilitation, and human-computer interaction.