Graph Convolutional Neural Networks with Data Augmentation for Single-Channel EEG-Based Seizure Classification
A deep-learning approach to seizure classification from single-channel EEG. We model EEG signals as graphs and apply Graph Convolutional Networks combined with data-augmentation strategies to overcome limited, imbalanced data — improving classification performance for automated seizure detection.