基于功能性腦網(wǎng)絡和圖卷積網(wǎng)絡的駕駛疲勞檢測

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

XU Junli

（JiangxiUniversityofTechnology，InnovationCenter，Nanchang3oo98，China）

Abstract：Toaddress the issue of ambiguous threshold criteria inconstructing functional brain networks（FBN） forfatiguedetection，thispaper proposed tosetafixed thresholdand employing graph convolutional networks （GCN）tooptimize the learning of brain network graph features.Athresholdof O.5 was setfor building the FBN， and the degree and clustering coeficientfeatures ofthe network were extracted.These features were then input into the GCN，which learned and optimized the graph features for detection classification.Theresults show that the preposed model's detectionaccuracy has reached 88.90% .Furthermore，degree centrality identifies 14 significant electrodes within thebrain network.Among them，the GCN modelbuilton7key electrodesachieves an 87.2% detection accuracy，with faster detection speed and superior overall performance compared to the detectionmodel based on 30 leads.

Keywords：graph convolutional networks （GCN）；functional brain networks （FBN）；simplified channels；driver fatigue

目前疲勞駕駛已經(jīng)成為引發(fā)交通事故的重要原因，研究者已經(jīng)采用了很多方法來檢測駕駛員的疲勞，其中腦電信號是疲勞檢測中常用的疲勞特征。（剩余11768字）