Recognising drivers’ mental fatigue based on EEG multi-dimensional feature selection and fusion-Reference-Cited by-同舟云学术

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Recognising drivers’ mental fatigue based on EEG multi-dimensional feature selection and fusion

Published:2023-01 Issue: Volume:79 Page:104237
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Zhang Yuhao,Guo Hanying,Zhou Yongjiang,Xu Chengji,Liao Yang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference52 articles.

1. Permutation entropy: a natural complexity measure for time series;Bandt;Phys. Rev. Lett.,2002

2. Feature-level fusion approaches based on multimodal EEG data for depression recognition;Cai;Information Fusion,2020

3. Correlation between driving errors and vigilance level: Influence of the driver’s age;Campagne;Physiol. Behav.,2004

4. Multi-channel EEG recordings during a sustained-attention driving task;Cao;Sci. Data,2019

5. Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males;Chen;Neuropsychologia,2019

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