A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals.
10.1007/s12264-024-01319-7
- Author:
Zijing GUAN
1
;
Xiaofei ZHANG
2
;
Weichen HUANG
3
;
Kendi LI
1
;
Di CHEN
1
;
Weiming LI
2
;
Jiaqi SUN
2
;
Lei CHEN
2
;
Yimiao MAO
2
;
Huijun SUN
3
;
Xiongzi TANG
3
;
Liping CAO
4
;
Yuanqing LI
5
Author Information
1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
2. The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, 510370, China.
3. Research Center for Brain-Computer Interface, Pazhou Lab, Guangzhou, 510330, China.
4. The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, 510370, China. 2008760504@gzhmu.edu.cn.
5. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China. auyqli@scut.edu.cn.
- Publication Type:Validation Study
- Keywords:
Brain-computer interface;
Depression detection;
EEG;
Multimodal
- MeSH:
Humans;
Male;
Female;
Adolescent;
Case-Control Studies;
Depression/diagnosis*;
Early Diagnosis;
Rest;
Electroencephalography/methods*;
Brain-Computer Interfaces;
Models, Psychological;
Reproducibility of Results;
Affect/physiology*;
Photic Stimulation/methods*;
Video Recording;
Brain/physiopathology*
- From:
Neuroscience Bulletin
2025;41(3):434-448
- CountryChina
- Language:English
-
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.
