典型文献
E3GCAPS:Efficient EEG-Based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection
文献摘要：
Cognitive state detection using electroen-cephalogram (EEG) signals for various tasks has at-tracted significant research attention.However,it is difficult to further improve the performance of cross-subject cognitive state detection.Further,most of the existing deep learning models will degrade sig-nificantly when limited training samples are given,and the feature hierarchical relationships are ignored.To address the above challenges,we propose an effi-cient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state de-tection,termed as Efficient EEG-based Multi-Capsule Framework (E3GCAPS).Specifically,we use a self-expression module to capture the potential connec-tions between samples,which is beneficial to alle-viate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG.In addition,considering the strong correlation between cognitive states and brain function connection mode,the dynamic subcapsule-based spatial attention mech-anism is introduced to explore the spatial relation-ship of multi-channel 1D EEG data,in which multi-channel 1D data greatly improving the training effi-ciency while preserving the model performance.The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset (FAAD) and the SJTU Emotion EEG Dataset (SEED).Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection un-der different tasks.
文献关键词：
中图分类号：
作者姓名：
Yue Zhao;Guojun Dai;Xin Fang;Zhengxuan Wu;Nianzhang Xia;Yanping Jin;Hong Zeng
作者机构：
School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China;Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province,Hangzhou 310018,China
文献出处：
引用格式：
[1]Yue Zhao;Guojun Dai;Xin Fang;Zhengxuan Wu;Nianzhang Xia;Yanping Jin;Hong Zeng-.E3GCAPS:Efficient EEG-Based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection)[J].中国通信（英文版）,2022(02):73-89
A类：
E3GCAPS,electroen,cephalogram,subcapsule,Awake,FAAD
B类：
Efficient,EEG,Based,Multi,Capsule,Framework,Dynamic,Attention,Cross,Subject,Cognitive,State,Detection,detection,using,signals,various,tasks,has,tracted,significant,research,attention,However,difficult,further,improve,performance,cross,subject,cognitive,Further,most,existing,deep,learning,models,will,degrade,nificantly,when,limited,training,samples,are,given,feature,hierarchical,relationships,ignored,To,address,above,challenges,propose,effi,interpretation,multiple,networks,termed,Specifically,self,expression,module,capture,potential,between,which,beneficial,viate,sensitivity,outliers,that,caused,by,individual,differences,In,addition,considering,strong,correlation,states,brain,function,connection,dynamic,spatial,mech,anism,introduced,explore,channel,1D,data,greatly,improving,ciency,while,preserving,effectiveness,validated,Fatigue,Dataset,SJTU,Emotion,SEED,Experimental,results,show,achieve,remarkable,different
AB值：
0.506017
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