A Method for Detecting Depression in Adolescence Based on an Affective Brain‑Computer Interface and Resting‑State Electroencephalogram Signals  

作　　者：Zijing Guan Xiaofei Zhang Weichen Huang Kendi Li Di Chen Weiming Li Jiaqi Sun Lei Chen Yimiao Mao Huijun Sun Xiongzi Tang Liping Cao Yuanqing Li 

机构地区：[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

出　　处：《Neuroscience Bulletin》2025年第3期434-448,共15页神经科学通报(英文版)

基　　金：supported by the STI 2030 Major Projects(2022ZD0211700);the Key R&D Program of Guangdong Province,China(2018B030339001);the Key Realm R&D Program of Guangzhou,China(202007030007);the National Natural Science Foundation of China(82371538);The authors gratefully acknowledge the approval granted by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University for this study involving human participants,with the approval ID(2021)No.071.

摘　　要：Depression is increasingly prevalent among adolescents and can profoundly impact their lives.However,the early detection of depression is often hindered by the timeconsuming 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 multi-modal 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.

关 键 词：Depression detection Brain-computer interface EEG Multimodal 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]