Open access
2026
A Data- and Physical- Features Driven Graph Convolution Network for Explainable Prediction of Schizophrenia With Electroencephalography
Experiments on three EEG datasets of schizophrenia show that the proposed MCNN-DGCN model achieves promising accuracy of 99.55%, 99.40%, and 97.86%, respectively, demonstrating significant advantages over alternative approaches.
Bo Chen, Zhi-Bo Hou, Yi-Fei Cheng et al.
· IEEE Access · 0 citations