Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel
Author
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-31
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health.
The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel.
Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers.
Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF).
The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications.
The best combination of channel + features + classifier is subject-specific.
In this work, the accuracy of FE as the feature is far greater than the Acc of other features.
The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst.
The impact of channel selection on the Acc is larger.
The performance of various channels is very different.
American Psychological Association (APA)
Hu, Jianfeng. 2017. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142162
Modern Language Association (MLA)
Hu, Jianfeng. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142162
American Medical Association (AMA)
Hu, Jianfeng. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142162
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142162