ArticleFrontiers in neuroscience2023
Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electroencephalogram for the Diagnosis of Depression: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.Brain topography · 2026Pooled it
- Hemispheric asymmetries and network dysfunctions in adolescent depression: A neuroimaging study using resting-state functional magnetic resonance imaging.World journal of psychiatry · 2025Trial
- Disentangling individual heterogeneity reveals robust network and molecular signatures of major depressive disorder with suicidal ideation.Translational psychiatry · 2026Article
- Utility of Functional Near-infrared Spectroscopy (fNIRS) in Major Depressive Disorder: A Systematic Review.Indian journal of psychological medicine · 2026Review
- Machine learning classification of fNIRS signals in patients with major depressive disorder and healthy controls.Frontiers in psychiatry · 2026Article
- Heterogeneity-Aware, Multiscale Annotation of Shared and Specific Neurobiological Signatures among Major Neurodevelopmental Disorders.Research (Washington, D.C.) · 2026Article
- Machine Learning Based Identification of Depressive Symptoms Among Students in a Chinese University Using Functional Near-Infrared Spectroscopy.Alpha psychiatry · 2025Article
- Research on drug addiction detection based on AR-TSNET with bimodal EEG-NIRS.Biomedical engineering online · 2025Article
- Cognitively Inspired Federated Learning Framework for Interpretable and Privacy-Secured EEG Biomarker Prediction of Depression Relapse.Bioengineering (Basel, Switzerland) · 2025Article
- Compact Colocated Bimodal EEG/fNIRS Multi-Distance Sensor.Sensors (Basel, Switzerland) · 2025Article
- Integrating Cortical Source Reconstruction and Adversarial Learning for EEG Classification.Sensors (Basel, Switzerland) · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications.Brain sciences · 2024Review
- Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis-Toward Retinal Metabolic Diagnostics.Diagnostics (Basel, Switzerland) · 2024Article
- Review
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14 authors.
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Abstract
Depression is a common mental disorder that seriously affects patients' social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven automated method to select features, and established a support vector machine model for automatic depression classification. The results showed the classification accuracy was 81.8% when using EEG features alone and increased to 92.7% when using hybrid EEG and fNIRS features. The brain network local efficiency in the delta band, hemispheric asymmetry in the theta band and brain oxygen sample entropy features differed significantly between the two groups (
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