Enhancing EEG-Based Schizophrenia Diagnosis with Explainable Multi-Branch Deep Learning

• Published in: IEEE journal of biomedical and health informatics Vol. PP; pp. 1 - 12
• Main Authors: Chang, Yu-Hsin, Huang, Yih-Ning, Chou, Jing-Lun, Lin, Huang-Chi, Wei, Chun-Shu
• Format: Journal Article
• Language: English
• Published: United States IEEE 29.07.2025
• Subjects: Accuracy; Adaptation models; Biological system modeling; Brain modeling; deep learning (DL); Electroencephalography; electroencephalography (EEG); explainable artificial intelligence (XAI); Feature extraction; Medical diagnostic imaging; Recording; Schizophrenia; Training
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3593647

Schizophrenia poses diagnostic challenges due to a lack of objective assessment. We propose MBSzEEGNet, a multi-branch deep-learning (DL) model for robust and interpretable EEG-based schizophrenia classification. Its specialized branches capture oscillatory and spatial-spectral features, enhancing generalization across two resting-state schizophrenia EEG datasets. MBSzEEGNet consistently outperforms leading DL architectures, achieving up to 85.71% subject-wise accuracy on one dataset and 75.64% on the other. Saliency-based explanations highlight potential biomarkers in the delta (0.5-4 Hz) and alpha (8-12 Hz) bands and the temporal and right parietal region. Our findings suggest that integrating explainable multi-branch DL architecture with EEG can enhance schizophrenia diagnosis and provide deeper insights into schizophrenia-related neural markers.