Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel
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 entropi...
Main Author: | |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2017-01-01
|
Series: | Computational and Mathematical Methods in Medicine |
Online Access: | http://dx.doi.org/10.1155/2017/5109530 |
id |
doaj-198b27ab908247d5b9fd27fa68720cb1 |
---|---|
record_format |
Article |
spelling |
doaj-198b27ab908247d5b9fd27fa68720cb12020-11-24T22:41:53ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182017-01-01201710.1155/2017/51095305109530Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG ChannelJianfeng Hu0Jiangxi University of Technology, Nanchang 330098, ChinaDriver 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.http://dx.doi.org/10.1155/2017/5109530 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianfeng Hu |
spellingShingle |
Jianfeng Hu Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel Computational and Mathematical Methods in Medicine |
author_facet |
Jianfeng Hu |
author_sort |
Jianfeng Hu |
title |
Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel |
title_short |
Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel |
title_full |
Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel |
title_fullStr |
Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel |
title_full_unstemmed |
Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel |
title_sort |
comparison of different features and classifiers for driver fatigue detection based on a single eeg channel |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2017-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2017/5109530 |
work_keys_str_mv |
AT jianfenghu comparisonofdifferentfeaturesandclassifiersfordriverfatiguedetectionbasedonasingleeegchannel |
_version_ |
1725700468742553600 |