{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T01:28:44Z","timestamp":1791941324226,"version":"4.3.4"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Research &amp; Innovation, Ministry of Education in Saudi Arabia","doi-asserted-by":"publisher","award":["RI-44-0176"],"award-info":[{"award-number":["RI-44-0176"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals\u2019 complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient\u2019s status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.<\/jats:p>","DOI":"10.3390\/diagnostics13101779","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T06:32:58Z","timestamp":1684391578000},"page":"1779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6019-5960","authenticated-orcid":false,"given":"Amel","family":"Ksibi","sequence":"first","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Zakariah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Riyadh 11442, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6664-5923","authenticated-orcid":false,"given":"Leila Jamel","family":"Menzli","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9520-3174","authenticated-orcid":false,"given":"Oumaima","family":"Saidani","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latifah","family":"Almuqren","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosy Awny Mohamed","family":"Hanafieh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computing in Al-Qunfudah, Umm Al-Qura University, Makkah 24382, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100102","DOI":"10.1016\/j.medntd.2021.100102","article-title":"Depression diagnosis by deep learning using EEG signals: A systematic review","volume":"12","author":"Safayari","year":"2021","journal-title":"Med. Nov. Technol. Devices"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100039","DOI":"10.1016\/j.neuri.2022.100039","article-title":"A deep learning-based comparative study to track mental depression from EEG data","volume":"2","author":"Sarkar","year":"2022","journal-title":"Neurosci. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1345-y","article-title":"Automated depression detection using deep representation and sequence learning with EEG signals","volume":"43","author":"Ay","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1007\/s13246-020-00938-4","article-title":"EEG-based deep learning model for the automatic detection of clinical depression","volume":"43","author":"Thoduparambil","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, J., Hou, K., Hu, B., Shen, J., and Pan, J. (2020, January 20\u201324). EEG-based depression detection using convolutional neural network with demographic attention mechanism. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175956"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1007\/s11517-019-01959-2","article-title":"EEG-based mild depression recognition using convolutional neural network","volume":"57","author":"Li","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y., Shen, Y., Fan, X., Huang, X., Yu, H., Zhao, G., and Ma, W. (2022). A novel EEG-based major depressive disorder detection framework with two-stage feature selection. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-01956-w"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1017\/S0033291720005358","article-title":"Increases in depression and anxiety symptoms in adolescents and young adults during the COVID-19 pandemic","volume":"52","author":"Hawes","year":"2022","journal-title":"Psychol. Med."},{"key":"ref_9","first-page":"323","article-title":"Detection of mental stress using EEG signals","volume":"4","author":"Natasha","year":"2018","journal-title":"Int. J. Eng. Tech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.cmpb.2018.04.012","article-title":"Automated EEG-based screening of depression using deep convolutional neural network","volume":"161","author":"Acharya","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106007","DOI":"10.1016\/j.cmpb.2021.106007","article-title":"EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review","volume":"202","author":"Yasin","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11137","DOI":"10.1007\/s00521-018-03980-2","article-title":"Acharya, Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals","volume":"32","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e34474","DOI":"10.2196\/34474","article-title":"Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach","volume":"25","author":"Kim","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.neurobiolaging.2018.07.004","article-title":"Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging","volume":"71","author":"Scally","year":"2018","journal-title":"Neurobiol. Aging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119096","DOI":"10.1016\/j.eswa.2022.119096","article-title":"Classification of image encoded SSVEP-based EEG signals using Convolutional Neural Networks","volume":"214","author":"Fantinato","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"011401","DOI":"10.1149\/2754-2726\/acb2da","article-title":"AI Enabled Ensemble Deep Learning Method for Automated Sensing and Quantification of DNA Damage in Comet Assay","volume":"2","author":"Mehta","year":"2023","journal-title":"ECS Sens. Plus"},{"key":"ref_17","first-page":"1979","article-title":"Awareness of mental health among teenagers","volume":"11","author":"Swetaa","year":"2019","journal-title":"Drug Invent. Today"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100616","DOI":"10.1016\/j.measen.2022.100616","article-title":"Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface","volume":"25","author":"Dhiman","year":"2023","journal-title":"Meas. Sens."},{"key":"ref_19","unstructured":"Iannetti, G.D., and Mouraux, A. (2023). EEG-fMRI: Physiological Basis, Technique, and Applications, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.sleep.2022.11.027","article-title":"Comparison of polysomnography in people with Alzheimer\u2019s disease and insomnia versus non-demented elderly people with insomnia","volume":"101","author":"Tao","year":"2023","journal-title":"Sleep Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.procs.2023.01.030","article-title":"Comparative Approach to Detect Nocturnal Frontal Lobe Epilepsy Sleep Disorder through Frequency spectrum and its Energy Levels","volume":"218","author":"Tiwari","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111582","DOI":"10.1016\/j.pscychresns.2022.111582","article-title":"Depression signal correlation identification from different EEG channels based on CNN feature extraction","volume":"328","author":"Wang","year":"2023","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1177\/00912174211068361","article-title":"Sleep-deprived electroencephalography, a forgotten investigation in psychiatry; a case series","volume":"58","author":"Najand","year":"2023","journal-title":"Int. J. Psychiatry Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"V\u00e4yrynen, T., Helakari, H., Korhonen, V., Tuunanen, J., Huotari, N., Piispala, J., Kallio, M., Raitamaa, L., Kananen, J., and J\u00e4rvel\u00e4, M. (2023). Sleep specific changes in infra-slow and respiratory frequency drivers of cortical EEG rhythms. bioRxiv, 2001\u20132023.","DOI":"10.1101\/2023.01.20.524831"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103262","DOI":"10.1016\/j.ipm.2022.103262","article-title":"SmartRolling: A human--machine interface for wheelchair control using EEG and smart sensing techniques","volume":"60","author":"Jiang","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110674","DOI":"10.1016\/j.pnpbp.2022.110674","article-title":"Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning","volume":"121","author":"Massaneda","year":"2023","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5165","DOI":"10.1109\/JSEN.2023.3239507","article-title":"EEG-based Emotion Identification Using One-Dimensional Deep Residual Shrinkage Network with Microstate Features","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101943","DOI":"10.1016\/j.jocs.2023.101943","article-title":"Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning","volume":"67","author":"Varli","year":"2023","journal-title":"J. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.bbe.2020.02.002","article-title":"A comparative analysis of signal processing and classification methods for different applications based on EEG signals","volume":"40","author":"Khosla","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102393","DOI":"10.1016\/j.bspc.2020.102393","article-title":"DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression","volume":"66","author":"Sharma","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3053999","article-title":"DeprNet: A deep convolution neural network framework for detecting depression using EEG","volume":"70","author":"Seal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11571-020-09619-0","article-title":"Major depressive disorder diagnosis based on effective connectivity in EEG signals: A convolutional neural network and long short-term memory approach","volume":"15","author":"Saeedi","year":"2021","journal-title":"Cogn. Neurodyn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8835","DOI":"10.1109\/ACCESS.2021.3049427","article-title":"Automated diagnosis of major depressive disorder using brain effective connectivity and 3D convolutional neural network","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/rob.21918","article-title":"A survey of deep learning techniques for autonomous driving","volume":"37","author":"Grigorescu","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1001\/jamainternmed.2018.7117","article-title":"Deep learning in medicine\u2014Promise, progress, and challenges","volume":"179","author":"Wang","year":"2019","journal-title":"JAMA Intern. Med."},{"key":"ref_36","unstructured":"Qayyum, A., Razzak, I., and Mumtaz, W. (2020). Neural Information Processing, Proceedings of the 27th International Conference, ICONIP 2020, Part III 27, Bangkok, Thailand, 23\u201327 November 2020, Springer."},{"key":"ref_37","first-page":"17","article-title":"An EEG-based depression detection method using machine learning model","volume":"10","author":"Bai","year":"2021","journal-title":"Int. J. Pharma. Med. Biol. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, W., Jia, K., Wang, Z., and Ma, Z. (2022). A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal. Brain Sci., 12.","DOI":"10.3390\/brainsci12050630"},{"key":"ref_39","first-page":"2165","article-title":"EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Physiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1177\/15500594211018545","article-title":"Depression diagnosis modeling with advanced computational methods: Frequency-domain eMVAR and deep learning","volume":"53","author":"Uyulan","year":"2022","journal-title":"Clin. EEG Neurosci."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1779\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:37:17Z","timestamp":1760125037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1779"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,17]]},"references-count":40,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["diagnostics13101779"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13101779","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,17]]}}}