A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.

backgroundMajor Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagnosis, treatment planning, and monitoring, yet current clinical tools are largely subjective, relying on self-report and clinician judgment via traditional assessment scales. EEG has emerged as a promising, non-invasive modality for capturing neural correlates of depression. However, most EEG-based machine learning diagnostic studies focus on boosting classification accuracy through complex algorithms and small, homogenous datasets. These black-box approaches often yield results that are difficult to interpret and poorly generalizable, making clinical translation impractical. Therefore there remains a critical need for models that are not only accurate but also transparent, robust, and grounded in the physiological properties of the data itself.

methodsWe proposed a data-centric, interpretable framework for EEG-based depression severity grading. A hybrid feature selection method was used, combining p-value and SHapley Additive exPlanations (SHAP) methods to select features that are both independently significant and jointly informative. The system was trained and evaluated on a large-scale, multi-site resting-state EEG dataset, using random forest for both classification and regression tasks. The SHAP method, an explainable artificial intelligence technique, is also used post-hoc to infer the key electrophysiological features and key brain regions associated with MDD mechanism to further increase interpretability.

resultsThe proposed system achieved 74.5% (95% CI [70.97%, 78.80%], p < 0.001) ten-fold classification accuracy and a correlation coefficient of 0.56 (95% CI [0.407, 0.683], p < 0.001) for severity estimation. SHAP analysis identified consistent, clinically meaningful EEG features, particularly in the left parietal-occipital lobe. Through in-depth SHAP value analysis, we identified critical disease-related brain areas in the left occipital and parietal lobes, along with key features including relative beta power in the left parietal lobe, time-domain features at the parietal midline, 1/f intercept, left occipital relative beta power, and global brain alpha energy.

conclusionThis study proposes a data-centric, interpretable depression grading system built on large-scale, multi-center EEG data, using simple models and hybrid feature selection to emphasize explainability, generalizability and data fidelity. By shifting the focus from algorithmic complexity to data transparency and feature-level insight, the model offers a practical and trustworthy path toward real-world mental health assessment.