A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.
Journal:
Journal of neuroengineering and rehabilitation
Published Date:
May 25, 2025
Abstract
BACKGROUND: Major 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.
