ArticleNeuroscience bulletin2025
A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals.
Article in Neuroscience bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Path Adversarial Dual-Branch Network for EEG Emotion Recognition.Sensors (Basel, Switzerland) · 2026Article
- Informed consent competency assessment for brain-computer interface clinical research and application in psychiatric disorders: A systematic review.World journal of psychiatry · 2025Article
- Can reinforcement learning effectively prevent depression relapse?World journal of psychiatry · 2025Review
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Authors and funding
13 authors.
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Abstract
Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
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