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

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.