Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic

Authors:
Chunlin Zhao;Chongxun Zheng;Min Zhao;Yaling Tu;Jianping Liu
Affiliations:
Institute of Biomedical Engineering, Xi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, No. 28, Xianning West Road, Xi'an, Shanxi 710049, PR Chi ...;Institute of Biomedical Engineering, Xi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, No. 28, Xianning West Road, Xi'an, Shanxi 710049, PR Chi ...;Institute of Biomedical Engineering, Xi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, No. 28, Xianning West Road, Xi'an, Shanxi 710049, PR Chi ...;Institute of Biomedical Engineering, Xi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, No. 28, Xianning West Road, Xi'an, Shanxi 710049, PR Chi ...;Engineering College of Armed Police Force, ShanQiao Road, Xi'an, Shanxi 710086, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Long-term driving is a significant cause of fatigue-related accidents. Driving mental fatigue has major implications for transportation system safety. Monitoring physiological signal while driving can provide the possibility to detect the mental fatigue and give the necessary warning. In this paper an EEG-based fatigue countermeasure algorithm is presented to classify the driving mental fatigue. The features of multichannel electroencephalographic (EEG) signals of frontal, central and occipital are extracted by multivariate autoregressive (MVAR) model. Then kernel principal component analysis (KPCA) and support vector machines (SVM) are employed to identify three-class EEG-based driving mental fatigue. The results show that KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve higher recognition accuracy (81.64%) of three driving mental fatigue states in 10 subjects. The KPCA-SVM method could be a potential tool for classification of driving mental fatigue.