ArticleJournal of Yeungnam medical science2024
Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review.
Article in Journal of Yeungnam medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Atypical Age-Related Patterns of Neural Signal Complexity in Autism Spectrum Disorder.CNS neuroscience & therapeutics · 2026Article
- Predicting big five personality traits from resting-state EEG connectivity: a connectome predictive modeling study.Brain structure & function · 2026Article
- Atieh Schizophrenia EEG, a novel high-quality dataset designed to advance biomarker and machine learning research.Scientific data · 2026Article
- Lymphocyte subpopulations and EEG asymmetry.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- DiagPat: An Explainable Language Detection Model Using EEG Signals.Sensors (Basel, Switzerland) · 2026Article
- 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.Bioengineering (Basel, Switzerland) · 2026Article
- Beta power as a neural correlate of sensory features in autistic individuals.Journal of neurodevelopmental disorders · 2026Article
- Neural Efficiency and Attentional Instability in Gaming Disorder: A Task-Based Occipital EEG and Machine Learning Study.Bioengineering (Basel, Switzerland) · 2026Article
- Quantitative electroencephalography as a next-generation tool in neurodiagnostics: significance, clinical applications, and practical interpretative frameworks.Frontiers in neuroscience · 2026Review
- Electroencephalography in hepatic encephalopathy: diagnostic and prognostic applications across the disease spectrum.Annals of gastroenterology · 2026Review
- Role of Electroencephalographic Biomarkers as Predictors of Post-Stroke Cognitive Outcomes in Patients with Cerebral Infarction: Literature Review.Neuropsychiatric disease and treatment · 2026Review
- Disrupted Global Brain Dynamics in Adolescents With Comorbid Anxiety and Depression: Neural Mechanisms and Classification Based on EEG Microstates.Depression and anxiety · 2026Article
- A computational pipeline for a neurotransmitter-centric analysis of the effects of psychiatric medication on EEG spectral power.Frontiers in psychiatry · 2026Article
- HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage.Scientific data · 2025Article
- Predicting Major Depressive Disorder Using Neural Networks from Spectral Measures of EEG Data.Bioengineering (Basel, Switzerland) · 2025Article
- High alpha oscillations in portable prefrontal EEG indicate gender-sensitive biomarkers for emotional disorders.Scientific reports · 2025Article
- Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis.Brain sciences · 2025Review
- Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Interhemispheric EEG coherence as a candidate biomarker in gambling disorder: evidence of frontal hyperconnectivity and posterior disconnectivity.Frontiers in neuroscience · 2025Article
- Development and validation of a multimodal data collection system for adolescent mental health management.Digital healthArticle
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1 author.
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Abstract
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.
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