{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:12:44Z","timestamp":1787670764718,"version":"build-2736575974"},"reference-count":70,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi\u2019s fractal dimension, and Katz\u2019s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.<\/jats:p>","DOI":"10.3390\/bios11120499","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T22:18:42Z","timestamp":1638829122000},"page":"499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7349-0731","authenticated-orcid":false,"given":"Chien-Te","family":"Wu","sequence":"first","affiliation":[{"name":"International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo 113-0033, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao-Chuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I-Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan"},{"name":"Institute of Health Policy and Management, National Taiwan University, Taipei 10617, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4129-0879","authenticated-orcid":false,"given":"Shih-Cheng","family":"Liao","sequence":"additional","affiliation":[{"name":"Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Ken","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan"},{"name":"College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6889-4686","authenticated-orcid":false,"given":"Chemin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan"},{"name":"College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shwu-Hua","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan"},{"name":"Department of Psychiatry, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu-Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan"},{"name":"Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Fen","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan"},{"name":"Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Hsin","family":"Weng","sequence":"additional","affiliation":[{"name":"Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Wei","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Bio Science & Tech., National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8377-2166","authenticated-orcid":false,"given":"Tzyy-Ping","family":"Jung","sequence":"additional","affiliation":[{"name":"Institute for Neural Computation, University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2536-1606","authenticated-orcid":false,"given":"Yi-Hung","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1016\/S0140-6736(11)60602-8","article-title":"Major depressive disorder: New clinical, neurobiological, and treatment perspectives","volume":"379","author":"Kupfer","year":"2012","journal-title":"Lancet"},{"key":"ref_2","first-page":"1517","article-title":"Depression Is the Leading Cause of Disability around the World","volume":"317","author":"Friedrich","year":"2017","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s40273-021-01019-4","article-title":"The Economic Burden of Adults with Major Depressive Disorder in the United States (2010 and 2018)","volume":"39","author":"Greenberg","year":"2021","journal-title":"PharmacoEconomics"},{"key":"ref_4","first-page":"1789","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: A systematic analysis for the Global Burden of Dis-ease Study","volume":"392","author":"Vos","year":"2017","journal-title":"Lancet"},{"key":"ref_5","unstructured":"World Health Organization (WHO) (2021, December 05). 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