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EEG - based driver fatigue detection methods

Author: NanZuoFen
Tutor: AiLingMei
School: Shaanxi Normal University
Course: Applied Computer Technology
Keywords: EEG driving fatigue S-Transform HHT(Hilbert-Huang Transform) bispetrum
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 128
Quote: 0
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Abstract


Along with the social progress in China, the domestic automotive industry is expanding rapidly. At the same time, traffic violations become more and more serious. Especially fatigue driving has been the "leading killer" of traffic accident. Therefore, a rational interference in driving behavor by studying driving fatigue detection techniques has an important significance.All the time, EEG is considered as the gold standard for detecting fatigue. However, previous studies on driving fatigue detection based on EEG are too simple to reveal exactly fatigue features. Research demonstrates that physical and mental status are shown objectively by EEG Based on the above states, this study extracts the EEG signals from 12 participants that are recorded successfully based on the close experiment project to explore the key features of fatigue.Firstly, a simulated signal whose frequency componets are given is analyzed by STFT, wavelet,WVD and S-Transform.A comparison of the results shows S-Transform has the peculiar advantages during the process of multi-frequency signal analysis, therefore S-Transform is here used to analyze EEG S-Transform is adopted to handle EEG signals in the process of driving, representing different time-frequency specturms of S-Transform at different driving times. This indicates the mental state changes with driving, which shows S-Transform is feasible as a technique for detecing driving fatigue.Secondly, based on the advantages of HHT over traditional time-frequency methods during processing two simulation signals with known frequency components, HHT is introduced into detecting driving fatigue. The time-frequency spectrums and Hilbert marginal spectrums of the EEG signals during driving are obtained through HHT, with different results at driving times. It indicates that HHT can detect driving fatigue behavior.Finally, from the angle of complexity and apting to be interfered in, the author adopts higher order spectrum with strong analysis ability and phase information. It is found that these three-dimensional images of the EEG signals using bispectrum are of great difference and some regular patterns exist at different times. Therfore, bispectrum can also serve as one method of driving fatigue detection. In sum, S-Transform, HHT and higher order spectrum can be used to extract EEG features in the process of driving, with a result whether a driver is at fatigue state, thus they might be considered as reference index of driving fatigue detection.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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