基于功能性脑网络和图卷积网络的驾驶疲劳检测  

Driver fatigue detection based on functional brain networks and graph convolutional networks

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作  者:徐军莉[1] XU Junli(Jiangxi University of Technology,Innovation Center,Nanchang 330098,China)

机构地区:[1]江西科技学院协同创新中心,南昌330098

出  处:《汽车安全与节能学报》2025年第2期226-233,共8页Journal of Automotive Safety and Energy

基  金:国家自然科学基金资助项目(61762045,61841201);江西省教育厅科技项目(GJJ2202611)。

摘  要:为了解决在疲劳检测中构建功能性脑网络(FBN)时,设置阈值标准较为模糊的问题,该文提出设置固定阈值,采用图卷积网络(GCN)来优化学习脑网络图特征。文中在构建FBN时设置阈值为0.5,提取脑网络的度和聚类系数特征,并输入GCN模型,模型对图特征进行学习优化,实现检测分类。结果表明:该模型检测的准确率可以达到88.90%;利用度中心性发现脑网络中的14个重要电极,其中基于7个重要电极构建的GCN模型检测的准确率为87.2%,检测速度更快,综合性能优于基于30导的检测模型。To address the issue of ambiguous threshold criteria in constructing functional brain networks(FBN)for fatigue detection,this paper proposed to set a fixed threshold and employing graph convolutional networks(GCN)to optimize the learning of brain network graph features.A threshold of 0.5 was set for building the FBN,and the degree and clustering coefficient features of the network were extracted.These features were then input into the GCN,which learned and optimized the graph features for detection classification.The results show that the preposed model's detection accuracy has reached 88.90%.Furthermore,degree centrality identifies 14 significant electrodes within the brain network.Among them,the GCN model built on 7 key electrodes achieves an 87.2%detection accuracy,with faster detection speed and superior overall performance compared to the detection model based on 30 leads.

关 键 词:图卷积网络(GCN) 功能性脑网络(FBN) 简化通道 驾驶疲劳 

分 类 号:U491.31[交通运输工程—交通运输规划与管理]

 

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