Driver Distraction State Recognition Based on Minimum Spanning Tree Features using EEG Data-Reference-Cited by-同舟云学术

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Driver Distraction State Recognition Based on Minimum Spanning Tree Features using EEG Data

Published:2023-11-10 Issue: Volume: Page:437-440
ISSN:
Container-title:2023 3rd International Conference on Digital Society and Intelligent Systems (DSInS)
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Author:

Liu Rui¹,Qi Geqi¹,Guan Wei¹,An Yaning¹

Affiliation:

1. Beijing Jiaotong University,Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport,Beijing,China,100044

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10455090/10455096/10455648.pdf?arnumber=10455648

Reference15 articles.

1. A Temporal-Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals

2. Cerebral cortical networking for mental workload assessment under various demands during dual-task walking

3. Detecting Distraction in Drivers using Electroencephalogram (EEG) Signals

4. Driving simulation study for the analysis of distraction effects in longitudinal driving behaviour

5. SAFECAR: A Brain–Computer Interface and intelligent framework to detect drivers’ distractions

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Survey of EEG-Based Driver State and Behavior Detection for Intelligent Vehicles;IEEE Transactions on Biometrics, Behavior, and Identity Science;2024-07

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