文献类型：期刊文献

英文题名：Driving EEG based multilayer dynamic brain network analysis for steering process

作者：Chang, Wenwen[1];Meng, Weiliang[2];Yan, Guanghui[1];Zhang, Bingtao[1];Luo, Hao[3];Gao, Rui[1];Yang, Zhifei[1]

第一作者：Chang, Wenwen

通信作者：Chang, WW[1];Meng, WL[2]

机构：[1]Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;[2]Chinese Acad Sci, LIAMA NLPR, Inst Automat, Beijing 100190, Peoples R China;[3]Gansu Univ Chinese Med, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China

第一机构：Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China

通信机构：[1]corresponding author), Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;[2]corresponding author), Chinese Acad Sci, LIAMA NLPR, Inst Automat, Beijing 100190, Peoples R China.

年份：2022

卷号：207

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20222912359892);Scopus(收录号：2-s2.0-85133862524);WOS:【SCI-EXPANDED(收录号:WOS:000830874300001)】；

基金：This work was supported by (i) the Science and Technology Project of Lanzhou City in China under grant 2021-1-150, (ii) the National Natural Science Foundation of China under grant 62062049, and grant 61962034, (iii) the Science and Technology Project of Gansu Province under grant 20JR10RA215, grant 20JR5RA390, and grant 20JR10RA240, and (iv) the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) under grant 202100020. Besides, this work was also supported by the Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University.

语种：英文

外文关键词：Multi -layer Networks; Functional Connectivity; Electroencephalogram (EEG); Driving Intention; Feature Extraction; Driving Behavior

摘要：Objectives: EEG-based brain computer interface has been demonstrated to be an effective tool for brain state and driving behavior detection to understand the human factors during driving. By providing a driving assistance operation consistent with the driver's action intention, it can improve the interaction process between driving system and its driver. Driving is a comprehensive process that requires the coordination of different brain regions. Functional connectivity, especially the dynamic connectivities calculated by statistical interdependencies between neural oscillations within these brain regions, which can provide some specific information for driving behavior.Methods & experiments: We developed a novel multi-layer brain network model for steering action to improve the understanding of dynamic characteristics during driving. Firstly, a simulated driving experiment is designed and participants were required to drive along a specified route to complete the left turn, right turn and straight action when arriving at an intersection, and electroencephalographic (EEG) signals were recorded simultaneously using a 32-channel system. Then, a multi-layer network framework which combined with an oscillatory envelope based functional connectivity metrics was designed to present the dynamic process of the driving.Results: The result shows there exist significant difference in the multi-layer network structure among the three steering conditions, especially between steering and straight moving. The corresponding parameter analysis also found the significant difference of multilayer modularity (Q-value) and multiplex participation coefficient (MPC) value among the three conditions. Further analysis about single network found the averaged degree, global efficiency, and clustering coefficient also shows significant difference between straight moving and steering action.Conclusion: We conclude that the multi-layer network model can more truly present the dynamic process during driving and provide more accurate information from spatial domain. Besides, the MPC and Q-Value are two new network markers can be used for the recognition of expected steering action, while the average value of corresponding super-matrix can also be used for straight driving and steering action recognition. Implication: The results demonstrate the feasibility of multilayer dynamic brain networks in driving behavior recognition, provided a new insight for the EEG based driving behavior recognition.