ArticleBiosensors2021
Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.
Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Infraslow Closed-Loop Brain Training for Anxiety and Depression (ISAD): A pilot randomised, sham-controlled trial in adult females with internalizing disorders.Cognitive, affective & behavioral neuroscience · 2025Trial
- FSC-BiMamba: A Dual-Branch Bidirectional Mamba Network for Frontal Sparse-Channel EEG Depression Detection.Brain sciences · 2026Article
- Increased beta-band salience network connectivity predicts 12-month longitudinal changes in depressive and anxiety symptom severity in young adults: a resting-state EEG study.European archives of psychiatry and clinical neuroscience · 2026Article
- A time-scale representation of EEG Higuchi fractal dimension.Scientific reports · 2026Article
- Anticipatory pleasure as a key hedonic component in classifying major depressive disorder: multidimensional behavioral evidence from majority vote algorithm.BMC psychiatry · 2026Article
- MS-MDDNet: A Lightweight Deep Learning Framework for Interpretable EEG-Based Diagnosis of Major Depressive Disorder.Diagnostics (Basel, Switzerland) · 2026Article
- Task-state P300 and functional brain network abnormalities in adolescent major depressive disorder: a Stroop paradigm study.Frontiers in psychiatry · 2026Article
- Case Report: Intranasal esketamine and accelerated intermittent theta-burst stimulation for severe treatment-resistant depression with suicidal ideation.Frontiers in psychiatry · 2026Article
- Comparison of overnight trends in relative power for specific frequency bands, sleep stages, and brain regions between patients with depressive disorder and matched control subjects.Psychiatry research. Neuroimaging · 2025Article
- The fractal dimension of resting state EEG increases over age in children.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer's Disease and Mild Cognitive Impairment.Biosensors · 2025Article
- Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121.Brain sciences · 2024Article
- Power spectral density-based resting-state EEG classification of first-episode psychosis.Scientific reports · 2024Article
- Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.Scientific reports · 2024Article
- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.bioRxiv : the preprint server for biology · 2024Article
- Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices.Journal of personalized medicine · 2024Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.