Driving EEG based multilayer dynamic brain network analysis for steering process
文献类型：期刊论文
| 作者 | Chang, Wenwen3; Meng, Weiliang2 ; Yan, Guanghui3; Zhang, Bingtao3; Luo, Hao1; Gao, Rui3; Yang, Zhifei3
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| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
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| 出版日期 | 2022-11-30 |
| 卷号 | 207页码:17 |
| 关键词 | Multi -layer Networks Functional Connectivity Electroencephalogram (EEG) Driving Intention Feature Extraction Driving Behavior |
| ISSN号 | 0957-4174 |
| DOI | 10.1016/j.eswa.2022.118121 |
| 通讯作者 | Chang, Wenwen(changww2013@126.com) ; Meng, Weiliang(weiliang.meng@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | EMERGENCY BRAKING INTENTION ; DRIVER ; SIGNALS ; TIME |
| 资助项目 | Science and Technology Project of Lanzhou City in China[2021-1-150] ; National Natural Science Foundation of China[62062049] ; National Natural Science Foundation of China[61962034] ; Science and Technology Project of Gansu Province[20JR10RA215] ; Science and Technology Project of Gansu Province[20JR5RA390] ; Science and Technology Project of Gansu Province[20JR10RA240] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[202100020] ; Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000830874300001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | Science and Technology Project of Lanzhou City in China ; National Natural Science Foundation of China ; Science and Technology Project of Gansu Province ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49758]  |
| 专题 | 模式识别国家重点实验室_三维可视计算 |
| 通讯作者 | Chang, Wenwen; Meng, Weiliang |
| 作者单位 | 1.Gansu Univ Chinese Med, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 2.Chinese Acad Sci, LIAMA NLPR, Inst Automat, Beijing 100190, Peoples R China 3.Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chang, Wenwen,Meng, Weiliang,Yan, Guanghui,et al. Driving EEG based multilayer dynamic brain network analysis for steering process[J]. EXPERT SYSTEMS WITH APPLICATIONS,2022,207:17. |
| APA | Chang, Wenwen.,Meng, Weiliang.,Yan, Guanghui.,Zhang, Bingtao.,Luo, Hao.,...&Yang, Zhifei.(2022).Driving EEG based multilayer dynamic brain network analysis for steering process.EXPERT SYSTEMS WITH APPLICATIONS,207,17. |
| MLA | Chang, Wenwen,et al."Driving EEG based multilayer dynamic brain network analysis for steering process".EXPERT SYSTEMS WITH APPLICATIONS 207(2022):17. |
入库方式： OAI收割
来源：自动化研究所
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