Zhi-Bo Hou

A Data- and Physical- Features Driven Graph Convolution Network for Explainable Prediction of Schizophrenia With Electroencephalography

This paper proposes a new multi-branch graph neural network (GCN) architecture for predicting schizophrenia versus healthy subjects via resting-state Electroencephalography (EEG). It leverages on both data and physical driven features derived from power spectral density (PSD) and functional connectivity (FC) indicators...