ArticleFrontiers in neuroscience2023
EEG-based high-performance depression state recognition.
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 13 papers.
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13 citing papers in PubMed, 24 citations in OpenAlex.
- Advancement in therapeutic application of quantum dots in amyotrophic lateral sclerosis: current opportunities and challenges.Drug delivery and translational research · 2026Review
- A Comparative Analysis of EWT and EMD Techniques in the Diagnosis of Major Depressive Disorder Using EEG: Asymmetry Features and Explainability via SHAP.Diagnostics (Basel, Switzerland) · 2026Article
- Elucidating the pharmacological mechanism of Yangming-Kaixin-Yizhi formula in inhibiting neuronal ferroptosis via Nrf2 in Alzheimer's disease: a study combining network pharmacology, transcriptomics, and experimental validation.Metabolic brain disease · 2026Article
- Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion Model.Sensors (Basel, Switzerland) · 2026Article
- Interpretable machine-learning for depression classification based on a three-component EEG marker.Frontiers in computational neuroscience · 2026Article
- Obesity and Cognitive Function: Leptin Role Through Blood-Brain Barrier and Hippocampus.Molecular neurobiology · 2025Review
- A comprehensive EEG dataset of laser-evoked potentials for pain research.Scientific data · 2025Article
- Integrating Cortical Source Reconstruction and Adversarial Learning for EEG Classification.Sensors (Basel, Switzerland) · 2025Article
- Bioactive compound nanoparticles for Alzheimer's disease.Inflammopharmacology · 2025Review
- A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.Journal of neuroengineering and rehabilitation · 2025Article
- Moving beyond diagnostic labels in psychiatry: outcome-linked treatment modelling.General psychiatry · 2025Article
- A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.PloS one · 2024Article
- Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is a global disease that is harmful to people. Traditional identification methods based on various scales are not objective and accurate enough. Electroencephalogram (EEG) contains abundant physiological information, which makes it a new research direction to identify depression state. However, most EEG-based algorithms only extract the original EEG features and ignore the complex spatiotemporal information interactions, which will reduce performance. Thus, a more accurate and objective method for depression identification is urgently needed. In this work, we propose a novel depression identification model: W-GCN-GRU. In our proposed method, we censored six sensitive features based on Spearman's rank correlation coefficient and assigned different weight coefficients to each sensitive feature by AUC for the weighted fusion of sensitive features. In particular, we use the GCN and GRU cascade networks based on weighted sensitive features as depression recognition models. For the GCN, we creatively took the brain function network based on the correlation coefficient matrix as the adjacency matrix input and the weighted fused sensitive features were used as the node feature matrix input. Our proposed model performed well on our self-collected dataset and the MODMA datasets with a accuracy of 94.72%, outperforming other methods. Our findings showed that feature dimensionality reduction, weighted fusion, and EEG spatial information all had great effects on depression recognition.
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