EEG Feature Graph

EEG feature graphs represent brain activity as networks, enabling analysis of complex spatial and temporal relationships between brain regions using graph neural networks (GNNs). Current research focuses on optimizing graph construction methods, including leveraging neurophysiological knowledge and employing self-supervised learning to improve accuracy and efficiency in applications like seizure detection, emotion recognition, and motor imagery classification. These advancements are significantly impacting the fields of brain-computer interfaces and clinical neurology by enabling more accurate, efficient, and potentially automated diagnosis and monitoring of neurological conditions.