EEG Datasets

EEG datasets are collections of brainwave recordings used to study brain activity and develop brain-computer interfaces (BCIs). Current research focuses on improving the accuracy and interpretability of EEG classification using deep learning models like Transformers, convolutional neural networks (CNNs), and graph neural networks (GNNs), often coupled with techniques like data augmentation and federated learning to address data scarcity and heterogeneity. These advancements are crucial for improving the reliability and clinical applicability of EEG-based diagnostics and BCIs, impacting fields such as neurological disorder diagnosis, mental health assessment, and assistive technologies.