ArticleSensors (Basel, Switzerland)2024
Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Aperiodic Neural Activity in Clinical Investigations.The European journal of neuroscience · 2025Pooled it
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- Frequency-decomposed EEG microstate analysis reveals altered cross-frequency coordination in adolescent major depressive disorder.European archives of psychiatry and clinical neuroscience · 2026Article
- Latent profile analysis of depressive symptoms and its associations with problematic social media use and demographic characteristics among Chinese college students.BMC psychology · 2026Article
- Subject-Wise Depression Screening from Eight-Channel Resting-State EEG Using Asymmetry-Aware Spectral Features and Connectivity Ablation.Sensors (Basel, Switzerland) · 2026Article
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- EEG Unpredictability in the Resting State of Major Depressive Disorder: A Multidomain EEG Analysis.Brain topography · 2026Article
- MS-MDDNet: A Lightweight Deep Learning Framework for Interpretable EEG-Based Diagnosis of Major Depressive Disorder.Diagnostics (Basel, Switzerland) · 2026Article
- Age-Related Differences and Effects of Internalizing Symptoms on Aperiodic Neural Activity in Adolescents.Psychophysiology · 2026Article
- High alpha oscillations in portable prefrontal EEG indicate gender-sensitive biomarkers for emotional disorders.Scientific reports · 2025Article
- Alterations in aperiodic neural activity associated with major depressive disorder.Nature. Mental health · 2025Article
- Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters.Diagnostics (Basel, Switzerland) · 2025Article
- Brain neural mechanisms underlying VR enhanced aerobic exercise for mood enhancement in depressed adolescents.Frontiers in human neuroscience · 2025Article
- Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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3 authors.
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Abstract
Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.
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