A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights
This 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.
Authors
- Xiaorong Gao (ORCID: https://orcid.org/0000-0003-0499-2740)
- Anruo Shen (ORCID: https://orcid.org/0000-0003-1974-4045)
- Jingnan Sun
- Xiaogang Chen (ORCID: https://orcid.org/0000-0001-5223-2397)
Institutions
- Johns Hopkins University (US)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Johns Hopkins Medicine (US)
- Tsinghua University (CN)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2025-05-25
- DOI
- https://doi.org/10.1186/s12984-025-01645-5
- Citations
- 6
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 8.59
Funders
- National Natural Science Foundation of China
- Key Research and Development Program of Ningxia
- Special Project for Research and Development in Key areas of Guangdong Province