EEG Feature

EEG feature extraction aims to identify meaningful patterns in brainwave data for various applications, including cognitive state classification, disease detection, and brain-computer interfaces. Current research heavily utilizes deep learning models, such as convolutional neural networks (CNNs), transformers, and graph neural networks (GNNs), often incorporating attention mechanisms to enhance feature extraction and classification accuracy. These advancements are improving the reliability and interpretability of EEG analysis, leading to more robust and efficient applications in neuroscience and related fields.