Real driving environment EEG-based detection of driving fatigue using the wavelet scattering network.
Driving fatigue
EEG
Real driving environment
WSN
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
01 Dec 2023
01 Dec 2023
Historique:
received:
22
06
2023
revised:
29
09
2023
accepted:
11
10
2023
medline:
6
11
2023
pubmed:
15
10
2023
entrez:
14
10
2023
Statut:
ppublish
Résumé
Driving fatigue is one of the main factors leading to traffic accidents. So, it is necessary to detect driver fatigue accurately and quickly. To precisely detect driving fatigue in a real driving environment, this paper adopts a classification method for driving fatigue based on the wavelet scattering network (WSN). Firstly, electroencephalogram (EEG) signals of 12 subjects in the real driving environment are collected and categorized into two states: fatigue and awake. Secondly, the WSN algorithm extracts wavelet scattering coefficients of EEG signals, and these coefficients are used as input in support vector machine (SVM) as feature vectors for classification. The results showed that the average classification accuracy of 12 subjects reached 99.33%; the average precision rate reached 99.28%; the average recall rate reached 98.27%; the average F1 score reached 98.74%; and the average classification accuracy of the public data set SEED-VIG reached 99.39%. The average precision, recall rate and F1 score reached 99.27%, 98.41% and 98.83% respectively. In addition, the WSN algorithm is compared with traditional convolutional neural network (CNN), Sparse-deep belief networks (SDBN), Spatio-temporal convolutional neural networks (STCNN), Long short-term memory (LSTM), and other methods, and it is found that WSN has higher classification accuracy. Furthermore, this method has good versatility, providing excellent recognition effect on small sample data sets, and fast running time, making it convenient for real-time online monitoring of driver fatigue. Therefore, the WSN algorithm is promising in efficiently detecting driving fatigue state of drivers in real environments, contributing to improved traffic safety.
Sections du résumé
BACKGROUND
BACKGROUND
Driving fatigue is one of the main factors leading to traffic accidents. So, it is necessary to detect driver fatigue accurately and quickly.
NEW METHOD
METHODS
To precisely detect driving fatigue in a real driving environment, this paper adopts a classification method for driving fatigue based on the wavelet scattering network (WSN). Firstly, electroencephalogram (EEG) signals of 12 subjects in the real driving environment are collected and categorized into two states: fatigue and awake. Secondly, the WSN algorithm extracts wavelet scattering coefficients of EEG signals, and these coefficients are used as input in support vector machine (SVM) as feature vectors for classification.
RESULTS
RESULTS
The results showed that the average classification accuracy of 12 subjects reached 99.33%; the average precision rate reached 99.28%; the average recall rate reached 98.27%; the average F1 score reached 98.74%; and the average classification accuracy of the public data set SEED-VIG reached 99.39%. The average precision, recall rate and F1 score reached 99.27%, 98.41% and 98.83% respectively.
COMPARISON WITH EXISTING METHODS
METHODS
In addition, the WSN algorithm is compared with traditional convolutional neural network (CNN), Sparse-deep belief networks (SDBN), Spatio-temporal convolutional neural networks (STCNN), Long short-term memory (LSTM), and other methods, and it is found that WSN has higher classification accuracy.
CONCLUSION
CONCLUSIONS
Furthermore, this method has good versatility, providing excellent recognition effect on small sample data sets, and fast running time, making it convenient for real-time online monitoring of driver fatigue. Therefore, the WSN algorithm is promising in efficiently detecting driving fatigue state of drivers in real environments, contributing to improved traffic safety.
Identifiants
pubmed: 37838152
pii: S0165-0270(23)00202-9
doi: 10.1016/j.jneumeth.2023.109983
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
109983Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare no conflict of interests.