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JYMS : Journal of Yeungnam Medical Science

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Review article
Psychiatry and Mental Health
Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review
Seokho Yun
J Yeungnam Med Sci. 2024;41(4):261-268.   Published online September 9, 2024
DOI: https://doi.org/10.12701/jyms.2024.00668
  • 16,469 View
  • 227 Download
  • 15 Web of Science
  • 17 Crossref
AbstractAbstract PDF
Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

Citations

Citations to this article as recorded by  
  • Neural Efficiency and Attentional Instability in Gaming Disorder: A Task-Based Occipital EEG and Machine Learning Study
    Riaz Muhammad, Ezekiel Edward Nettey-Oppong, Muhammad Usman, Saeed Ahmed Khan Abro, Toufique Ahmed Soomro, Ahmed Ali
    Bioengineering.2026; 13(2): 152.     CrossRef
  • Development and validation of a multimodal data collection system for adolescent mental health management
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    DIGITAL HEALTH.2026;[Epub]     CrossRef
  • Multi-omics biomarkers in psychiatric disorders diagnosis and stratification
    Seyyed Hossein Khatami, Sanam Anoosheh, Marzieh Khodaparast, Amir Maghsoudloonejad, Ehsan Dadgostar, Amir Asadi, Mahya Kaveh, Malihe Mehdinejad Haghighi
    Clinica Chimica Acta.2026; 585: 120887.     CrossRef
  • Lymphocyte subpopulations and EEG asymmetry
    Matisse Ducharme, Reza Zomorrodi, George Nader, Corinne Fischer, Philip Gerretsen, Ariel Graff, Daniel Blumberger, Vincenzo De Luca
    Journal of Neural Transmission.2026;[Epub]     CrossRef
  • Beta power as a neural correlate of sensory features in autistic individuals
    Julie Chaudet, Julien Pichot, Amandine Pedoux, Mathis Fleury, Anna Maruani, Valérie Vantalon, Elise Humeau, Thomas Bourgeron, Josselin Houenou, Guillaume Dumas, Edouard Duchesnay, Richard Delorme, Anton Iftimovici, Aline Lefebvre
    Journal of Neurodevelopmental Disorders.2026;[Epub]     CrossRef
  • Role of Electroencephalographic Biomarkers as Predictors of Post-Stroke Cognitive Outcomes in Patients with Cerebral Infarction: Literature Review
    Di Pu, Yan Xiong
    Neuropsychiatric Disease and Treatment.2026; Volume 22: 1.     CrossRef
  • Novel Time-Series Forecasting Method to Enhance Accuracy of Real-Time EEG Detection for BCI-Based Neurofeedback Motor Training in Individuals with Cerebral Palsy and Other Neurological Disorders
    Andrew Gravunder, Amanda Studnicki, Julia Kline, Ahad Behboodi, Thomas C. Bulea, Diane L. Damiano
    Bioengineering.2026; 13(5): 561.     CrossRef
  • DiagPat: An Explainable Language Detection Model Using EEG Signals
    Tugce Keles, Kubra Yildirim, Dahiru Tanko, Suat Tas, Irem Tasci, Burak Tasci, Gulay Tasci, Turker Tuncer, Sengul Dogan
    Sensors.2026; 26(11): 3363.     CrossRef
  • Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
    Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer
    Diagnostics.2025; 15(2): 154.     CrossRef
  • Innovative Therapeutic Approaches in Severe Adolescent Depression: Neuroimaging and Pharmacological Insights
    Andrei-Gabriel Zanfir, Simona-Corina Trifu
    Balneo and PRM Research Journal.2025; 16(Vol 16 No.): 765.     CrossRef
  • Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis
    Lin Bai, Gerhard Litscher, Xiaoning Li
    Brain Sciences.2025; 15(6): 634.     CrossRef
  • A Systematic Review of Mental Health Monitoring and Intervention Using Unsupervised Deep Learning on EEG Data
    Akhila Reddy Yadulla, Guna Sekhar Sajja, Santosh Reddy Addula, Mohan Harish Maturi, Geeta Sandeep Nadella, Elyson De La Cruz, Karthik Meduri, Hari Gonaygunta
    Psychology International.2025; 7(3): 61.     CrossRef
  • A recent advances on autism spectrum disorders in diagnosing based on machine learning and deep learning
    Hajir Ammar Hatim, Zaid Abdi Alkareem Alyasseri, Norziana Jamil
    Artificial Intelligence Review.2025;[Epub]     CrossRef
  • High alpha oscillations in portable prefrontal EEG indicate gender-sensitive biomarkers for emotional disorders
    Shu Tang, Chuanliang Han, Xuebing Li
    Scientific Reports.2025;[Epub]     CrossRef
  • Interhemispheric EEG coherence as a candidate biomarker in gambling disorder: evidence of frontal hyperconnectivity and posterior disconnectivity
    Eda Yılmazer, Metin Çinaroğlu, Selami Varol Ülker, Sultan Tarlacı
    Frontiers in Neuroscience.2025;[Epub]     CrossRef
  • HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage
    Jian Shi, Danyang Chen, Xingwei Zhao, Zhixian Zhao, Shengjie Li, Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang
    Scientific Data.2025;[Epub]     CrossRef
  • Predicting Major Depressive Disorder Using Neural Networks from Spectral Measures of EEG Data
    Igor Kozulin, Ekaterina Merkulova, Vasiliy Savostyanov, Haonan Shi, Xinyi Wang, Andrey Bocharov, Alexander Savostyanov
    Bioengineering.2025; 12(11): 1251.     CrossRef
Focused Review article
Psychiatry and Mental Health
Understanding insomnia as systemic disease
Seokho Yun, Sohye Jo
Yeungnam Univ J Med. 2021;38(4):267-274.   Published online September 13, 2021
DOI: https://doi.org/10.12701/yujm.2021.01424
  • 18,520 View
  • 216 Download
  • 7 Crossref
AbstractAbstract PDF
Sleep plays a critical role in homeostasis of the body and mind. Insomnia is a disease that causes disturbances in the initiation and maintenance of sleep. Insomnia is known to affect not only the sleep process itself but also an individual’s cognitive function and emotional regulation during the daytime. It increases the risk of various neuropsychiatric diseases such as depression, anxiety disorder, and dementia. Although it might appear that insomnia only affects the nervous system, it is also a systemic disease that affects several aspects of the body, such as the cardiovascular, endocrine, and immune systems; therefore, it increases the risk of various diseases such as hypertension, diabetes mellitus, and infection. Insomnia has a wide range of effects on our bodies because sleep is a complex and active process. However, a high proportion of patients with insomnia do not seek treatment, which results in high direct and indirect costs. This is attributed to the disregard of many of the negative effects of insomnia. Therefore, we expect that understanding insomnia as a systemic disease will provide an opportunity to understand the condition better and help prevent secondary impairment due to insomnia.

Citations

Citations to this article as recorded by  
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    M. P. Divyamani, Sachin N. Hegde, S. K. Naveen Kumar, M. P. Dharma Guru Prasad
    ACS Omega.2025; 10(33): 38303.     CrossRef
  • Insomnia and Cardiometabolic Health: Bridging the Gap Between Sleep Deficit and Disease Prevention
    James W. Kim, Akshay B. Jain, Atul Khullar
    Canadian Journal of Diabetes.2025; 49(5-6): 342.     CrossRef
  • Medical service utilization patterns among adults with insomnia: A retrospective cohort study
    Min Kyung Hyun, Jang Won Lee
    European Journal of Integrative Medicine.2024; 67: 102325.     CrossRef
  • Exploring Health Promotion Behaviors, Occupational Burnout, and Sleep Disturbances in Traditional Industry Workers
    Ying-Fen Yu, Yi-Ya Chang, Shu-Hung Chang
    Healthcare.2024; 13(1): 51.     CrossRef
  • Socio-Ecological Context of Sleep: Gender Differences and Couples’ Relationships as Exemplars
    Andrea N. Decker, Alexandra R. Fischer, Heather E. Gunn
    Current Psychiatry Reports.2022; 24(12): 831.     CrossRef
  • Clinical Spectrum and Trajectory of Innovative Therapeutic Interventions for Insomnia: A Perspective
    Yun-Jo Lo, Viraj Krishna Mishra, Hung-Yao Lo, Navneet Kumar Dubey, Wen-Cheng Lo
    Aging and disease.2022;[Epub]     CrossRef
  • Understanding sleep and sleep disturbances in autism spectrum disorder, and management of insomnia: an update
    Hye-Geum Kim
    Yeungnam University Journal of Medicine.2021; 38(4): 265.     CrossRef

JYMS : Journal of Yeungnam Medical Science
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