A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals
Zijing Guan1,3 · Xiaofei Zhang2 · Weichen Huang3 · Kendi Li1,3 · Di Chen1,3 · Weiming Li2 · Jiaqi Sun2 · Lei Chen2 · Yimiao Mao2 · Huijun Sun3 · Xiongzi Tang3 · Liping Cao2 · Yuanqing Li1,31 School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
2 The Afliated Brain Hospital, Guangzhou Medical University, Guangzhou 510370, China
3 Research Center for Brain-Computer Interface, Pazhou Lab, Guangzhou 510330, China
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.
Keywords
Depression detection; Brain-computer interface; EEG; Multimodal
