清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
冷傲半邪完成签到,获得积分10
4秒前
冠冠冠冠发布了新的文献求助150
37秒前
喵叽关注了科研通微信公众号
44秒前
冠冠冠冠完成签到,获得积分10
45秒前
简单的雅蕊完成签到,获得积分10
45秒前
48秒前
52秒前
zsmj23完成签到 ,获得积分0
56秒前
1分钟前
t铁核桃1985完成签到 ,获得积分10
1分钟前
李健应助简单的雅蕊采纳,获得10
1分钟前
彭于晏应助Betty采纳,获得10
2分钟前
辣酒猫完成签到,获得积分20
2分钟前
辣酒猫发布了新的文献求助10
2分钟前
2分钟前
周周南完成签到 ,获得积分10
3分钟前
3分钟前
3分钟前
533发布了新的文献求助10
3分钟前
稻子完成签到 ,获得积分10
3分钟前
梨子茶发布了新的文献求助30
3分钟前
领导范儿应助Huck采纳,获得10
3分钟前
3分钟前
Huck发布了新的文献求助10
3分钟前
Huck完成签到,获得积分10
3分钟前
整齐的蜻蜓完成签到 ,获得积分10
3分钟前
4分钟前
zhang完成签到,获得积分10
4分钟前
4分钟前
小余同学发布了新的文献求助10
4分钟前
coolplex完成签到 ,获得积分10
4分钟前
4分钟前
4分钟前
lenny发布了新的文献求助10
4分钟前
迷茫的一代完成签到,获得积分10
4分钟前
可爱沛蓝完成签到 ,获得积分10
4分钟前
大模型应助科研通管家采纳,获得10
4分钟前
lenny完成签到,获得积分10
4分钟前
方白秋完成签到,获得积分10
5分钟前
5分钟前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968504
求助须知:如何正确求助?哪些是违规求助? 3513331
关于积分的说明 11167297
捐赠科研通 3248697
什么是DOI,文献DOI怎么找? 1794417
邀请新用户注册赠送积分活动 875030
科研通“疑难数据库(出版商)”最低求助积分说明 804652