{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:21:23Z","timestamp":1783570883294,"version":"3.55.0"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2020 National Social Science Fund","award":["20BTQ073"],"award-info":[{"award-number":["20BTQ073"]}]},{"name":"2020 National Social Science Fund","award":["2023SQ19"],"award-info":[{"award-number":["2023SQ19"]}]},{"name":"Special Fund for the \u2018Community Medicine and Health Management Research Project\u2019 of the Shanghai Society of Integrated Traditional Chinese and Western Medicine","award":["20BTQ073"],"award-info":[{"award-number":["20BTQ073"]}]},{"name":"Special Fund for the \u2018Community Medicine and Health Management Research Project\u2019 of the Shanghai Society of Integrated Traditional Chinese and Western Medicine","award":["2023SQ19"],"award-info":[{"award-number":["2023SQ19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Depression has become one of the most common mental illnesses, causing severe physical and mental harm. To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. The hybrid model employed K-nearest neighbors (KNN), decision tree (DT), and Extreme Gradient Boosting (XGBoost) as base learners and used a DT as the meta-learner. Compared with traditional single methods, the hybrid approach significantly improved detection accuracy by leveraging the strengths of different algorithms. In addition, this study divided the brain regions into the left and right temporal lobes and extracted both linear and nonlinear features to comprehensively capture the complexity and dynamic behavior of EEG signals, enhancing the model\u2019s ability to distinguish features across different brain regions. The experimental results showed that among the eight traditional machine learning methods, the KNN classifier achieved the highest detection accuracy of 96.97% in the left temporal lobe region. In contrast, the stacking hybrid learning model further increased the detection accuracy to 98.07%, significantly outperforming the single models. Moreover, the analysis of the brain region segmentation revealed that the left temporal lobe exhibited higher discriminative power in detecting MDD, highlighting its important role in the neurobiology of depression. This study provides a solid foundation for developing more efficient and portable methods for detecting depression, offering new perspectives and approaches for EEG-based MDD detection, and contributing to the improvement in objectivity and precision in depression diagnosis.<\/jats:p>","DOI":"10.3390\/bioengineering12050449","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T11:44:52Z","timestamp":1745495092000},"page":"449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9457-3986","authenticated-orcid":false,"given":"Qi","family":"Sang","sequence":"first","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China"},{"name":"Human Phenome Institute, Fudan University, Shanghai 201203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeguo","family":"Shao","sequence":"additional","affiliation":[{"name":"College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China"},{"name":"Shanghai University of Medical and Health Science Affiliated Zhoupu Hospital, Shanghai 201318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41572-023-00454-1","article-title":"Major depressive disorder","volume":"9","author":"Marx","year":"2023","journal-title":"Nat. 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