An Interpretable Hybrid Neural Network Integrating Sinc-Convolution and Transformer for EEG-Based Depression...

EEG recordings obtained before medication are regarded as valuable biological indicators for depression detection. Currently, depression diagnosis based on EEG using convolutional neural networks (CNNs) has achieved relatively high detection performance, but some issues remain unresolved. CNNs are constrained by their limited receptive fields, which restrict them to capturing local rather than global dependencies. In addition, the complex features learned by CNNs are often hard to interpret and typically require a substantial number of trainable parameters. To tackle these issues, an interpretable hybrid neural network named SINCFORMER-SHAP is proposed. SINCFORMER-SHAP comprises two main components, namely the spatial-frequency and temporal feature extraction modules. The spatial-frequency feature extraction module leverages a hybrid design, where temporal filtering through a sinc-based convolution is coupled with spatial convolution, enabling the model to learn fine-grained spatial-spectral patterns. The sinc-convolutional layer helps constrain the parameter count, enhancing model efficiency. Subsequently, the temporal domain feature extraction module utilizes Transformer to capture global time-domain dependencies. Kernel visualization is used to provide direct insights into the spectral features learned by the spatial-frequency feature extraction module. To further enhance interpretability on the spatial domain, a post-hoc analysis is conducted using SHAP method. Based on the results of interpretability analysis, potential biomarkers have been observed within alpha and gamma rhythms across the frontal, parietal, temporal, and occipital areas. Comprehensive experiments conducted on public MODMA, EDRA and Mumtaz datasets were used to assess the performance of the proposed approach. The experimental outcomes provide compelling evidence that the proposed method not only surpasses multiple state-of-the-art approaches in performance, but also contributes a significant advancement toward the development of interpretable diagnostic technique for depression, thereby bridging the gap between computational methodologies and practical psychiatric applications.