EEG Pattern

EEG pattern analysis focuses on extracting meaningful information from brainwave recordings to understand various cognitive and clinical states. Current research emphasizes developing robust and interpretable machine learning models, such as graph attention networks and deep learning architectures like LSTMs, to analyze both temporal and spatial aspects of EEG data for applications like emotion recognition and clinical diagnosis (e.g., identifying harmful brainwave patterns in critically ill patients). These advancements aim to improve the accuracy and efficiency of EEG interpretation, reducing human bias and potentially transforming clinical workflows and affective computing applications.