Graph convolutional neural networks (GCNs) have demonstrated effectiveness in processing graph structure. Due to the diversity and complexity of real-world graph data, heterogeneous GCN have attracted ...

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among ...

EEG Transformer + SHAP This project builds a machine learning pipeline for classifying states of consciousness from EEG-derived features using a transformer-based model. Predictions are explained ...