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Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals

可解释性 计算机科学 脑电图 人工智能 模式识别(心理学) 冗余(工程) 图形 特征提取 机器学习 理论计算机科学 心理学 操作系统 精神科
作者
Shuangyong Zhang,Hong Wang,Zixi Zheng,Tianyu Liu,Weixin Li,Zishan Zhang,Yanshen Sun
出处
期刊:International Journal of Neural Systems [World Scientific]
卷期号:33 (11) 被引量:8
标识
DOI:10.1142/s0129065723500557
摘要

Automated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure of EEG signals has not been fully utilized to capture more fine-grained features. (3) Prior depression detection models fail to provide interpretability. To address these challenges, this paper proposes a new model, Multi-view Graph Contrastive Learning via Adaptive Channel Optimization (MGCL-ACO) for depression detection in EEG signals. Specifically, the proposed model first selects the critical channels by maximizing the mutual information between tracks and labels of EEG signals to eliminate data redundancy. Then, the MGCL-ACO model builds two similarity metric views based on functional connectivity and spatial proximity. MGCL-ACO constructs the feature extraction module by graph convolutions and contrastive learning to capture more fine-grained features of different perspectives. Finally, our model provides interpretability by visualizing a brain map related to the significance scores of the selected channels. Extensive experiments have been performed on public datasets, and the results show that our proposed model outperforms the most advanced baselines. Our proposed model not only provides a promising approach for automated depression detection using optimal EEG signals but also has the potential to improve the accuracy and interpretability of depression diagnosis in clinical practice.
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