Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review

Diagnostics, Journal Year: 2025, Volume and Issue: 15(2), P. 210 - 210

Published: Jan. 17, 2025

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral to be disrupted varying degrees. One these is depression, a significant factor contributing the increase in suicide cases worldwide. Consequently, depression has become public health issue globally. Electroencephalogram (EEG) data can utilized diagnose mild disorder (MDD), offering valuable insights into pathophysiological mechanisms underlying mental enhancing understanding MDD. Methods: This survey emphasizes critical role EEG advancing artificial intelligence (AI)-driven approaches for diagnosis. By focusing on studies integrate with machine learning (ML) deep (DL) techniques, we systematically analyze methods utilizing signals identify biomarkers. The highlights advancements preprocessing, feature extraction, model development, showcasing how enhance diagnostic precision, scalability, automation detection. Results: distinguished from prior reviews by addressing their limitations providing researchers future studies. It offers comprehensive comparison ML DL an overview five key steps also presents existing datasets diagnosis critically analyzes limitations. Furthermore, it explores directions challenges, such as robustness augmentation techniques optimizing channel selection improved accuracy. potential transfer encoder-decoder architectures leverage pre-trained models performance discussed. Advancements extraction automated highlighted avenues improving performance. Additionally, integrating Internet Things (IoT) devices continuous monitoring distinguishing between different types identified research areas. Finally, review reliability predictability computational intelligence-based advance Conclusions: study will serve well-organized helpful reference working detecting using provide outlined above, guiding further field.

Language: Английский

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Time–frequency and functional connectivity analysis in drug‐naive adolescents with depression based on electroencephalography using a visual cognitive task: A comparative study

Journal of Child Psychology and Psychiatry, Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Previous research studies have demonstrated cognitive deficits in adolescents with depression; however, the neuroelectrophysiological mechanisms underlying these remain poorly understood. Utilizing electroencephalography (EEG) data collected during tasks, this study applies time-frequency analysis and functional connectivity (FC) techniques to explore alterations associated depression. A total of 173 depression 126 healthy controls (HC) participated study, undergoing EEG while performing a visual oddball task. Delta, theta, alpha power spectra, along FC, were calculated analyzed. Adolescents exhibited significantly reduced delta, at Fz, Cz, C5, C6, Pz, P5, P6 electrodes compared HC group. Notably, theta F5 electrode F6 lower group than Additionally, cortical FC frontal central regions was markedly decreased HC. During display distinct abnormalities both high- low-frequency brain oscillations, as well frontal, central, parietal These findings offer valuable insights into adolescent

Language: Английский

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Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 192, P. 110154 - 110154

Published: April 23, 2025

Language: Английский

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Smart Textile Technology for the Monitoring of Mental Health

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1148 - 1148

Published: Feb. 13, 2025

In recent years, smart devices have proven their effectiveness in monitoring mental health issues and played a crucial role providing therapy. The ability to embed sensors fabrics opens new horizons for healthcare, addressing the growing demand innovative solutions objective of this review is understand health, its impact on human body, latest advancements field textiles (sensors, electrodes, garments) physiological signals such as respiration rate (RR), electroencephalogram (EEG), electrodermal activity (EDA), electrocardiogram (ECG), cortisol, all which are associated with disorders. Databases Web Science (WoS) Scopus were used identify studies that utilized monitor specific parameters. Research indicates provide promising results compared traditional methods, offering enhanced comfort long-term monitoring.

Language: Английский

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Identification of Neurophysiological Signatures of Bipolar Disorder by Resting-State EEG Microstate Analysis

Journal of Affective Disorders Reports, Journal Year: 2025, Volume and Issue: unknown, P. 100891 - 100891

Published: Feb. 1, 2025

Language: Английский

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Alpha oscillation mediates the interaction between suicide risk and symptom severity in Major Depressive Disorder

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: Aug. 7, 2024

The aim of our study was to explore the relationship between changes in neural oscillatory power EEG, severity depressive-anxiety symptoms, and risk suicide MDD.

Language: Английский

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Deep Learning Analysis Approach for Major Depressive Disorder in Children and Adolescents

Published: Aug. 2, 2024

Abstract — This chapter reviews deep learning models previously used to identify neuro-biomarkers related major depressive disorder in adults and children, detailing the outcomes of prior research current advancements this field. In addition, explores use convolutional neural networks classify detect associated with comparison age-matched healthy individuals. Specifically, networks, utilizing visual geometry group (VGG16) DeprNet were applied analyze resting-state, eyes-closed electroencephalography (EEG) data. The EEG data undergoes thorough pre-processing, modules are employed facilitate understanding analysis These techniques aim extract interest within each frequency band across various regions interest, striving develop a robust generic method for interpreting model's "black box." approach incorporates several scoring methods, addresses imbalance, manages limited availability, optimizes hyper-parameters these models.

Language: Английский

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Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: July 10, 2024

Background Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis MDD susceptible to interference by several factors, which has led a trend exploring objective biomarkers. Electroencephalography (EEG) non-invasive procedure that being gradually applied detect and diagnose through some features such as functional connectivity (FC). Methods In this research, we analyzed resting-state EEG patients healthy controls (HCs) in both eyes-open (EO) eyes-closed (EC) conditions. phase locking value (PLV) method was utilized explore connection synchronization neuronal activities spatiotemporally between different brain regions. We compared PLV participants HCs five frequency bands (theta, 4–8 Hz; alpha, 8–12 beta1, 12–16 beta2, 16–24 beta3, 24–40 Hz) further correlation connections significant differences severity depression (via scores 17-item Hamilton Depression Rating Scale, HDRS-17). Results During EO period, lower PLVs were found right temporal-left midline occipital cortex (RT-LMOC; theta, beta2) posterior parietal-right temporal (PP-RT; beta1 group HC group, while higher LT-LMOC (beta2). EC for theta beta (beta1, beta3) PP-RT, well RT-LMOC. Additionally, left frontal cortex-right (LMFC-RT) parietal (PP-RMOC), observed beta2. There no correlations HDRS-17 when significantly (all p > 0.05) checked. Conclusion Our study confirmed presence FC individuals. Lower mostly observed, whereas an increase lobe (EO), circuits frontal-temporal lobe, parietal-occipital lobe. trends involved not correlated level depression. Limitations limited due lack analysis confounding factors follow-up data. Future studies large-sampled long-term designs are needed distinguishable individuals MDD.

Language: Английский

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The Effect of Induced Regulatory Focus on Frontal Cortical Activity

Behavioral Sciences, Journal Year: 2024, Volume and Issue: 14(4), P. 292 - 292

Published: April 1, 2024

The motivation–direction model has served as the primary framework for understanding frontal cortical activity. However, research on link between approach/avoidance motivation and left/right activity produced inconsistent findings. Recent studies suggest that regulatory systems may offer a more accurate explanation than motivational direction model. Despite being systems, relationship focus received limited attention. Only one experimental study explored this connection through correlational analysis, yet it lacks causal evidence. present aimed to address gap by manipulating measuring in 36 college students. Our results revealed induced promotion led increased left activity, whereas prevention right These findings enhance our physiological of deeper how influences alterations psychology behavior.

Language: Английский

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