Chunfeng Yang

G3DCT is a deep learning framework for EEG artifact identification that outperforms existing models, achieving a 2.8% F1-score improvement on combined artifacts.

The paper analyzes cortical wave dynamics in human brain development using iEEG data, finding age-related changes in wave speed and anatomical correlations.

The paper introduces a task-driven EEG dataset with channel-level motion artifact annotations and demonstrates a hybrid CNN-Transformer model for artifact detection and classification.