Driver Fatigue Detection Through Deep Transfer Learning in an Electroencephalogram-based System

Fei WANG; Shichao WU; Shaolin LIU; Yahui ZHANG; Ying WEI

doi:10.11999/JEIT180900

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2264-2272

Fei WANG, Shichao WU, Shaolin LIU, Yahui ZHANG, Ying WEI. Driver Fatigue Detection Through Deep Transfer Learning in an Electroencephalogram-based System[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2264-2272. doi: 10.11999/JEIT180900

Citation:

Fei WANG, Shichao WU, Shaolin LIU, Yahui ZHANG, Ying WEI. Driver Fatigue Detection Through Deep Transfer Learning in an Electroencephalogram-based System[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2264-2272. doi: 10.11999/JEIT180900

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Driver Fatigue Detection Through Deep Transfer Learning in an Electroencephalogram-based System

doi: 10.11999/JEIT180900

1.
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169 China
2.
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Funds: The Fundamental Research Funds for the Central Universities (N172608005), The Scientific Research Foundation for Public Welfare of Liaoning Province (20170021)

Received Date: 2018-09-20
Rev Recd Date: 2019-02-17

Available Online: 2019-03-21

Publish Date: 2019-09-10

Abstract

Abstract

ElectroEncephaloGram (EEG) is regarded as a " gold standard” of fatigue detection and drivers’ vigilance states can be detected through the analysis of EEG signals. However, due to the characteristics of non-linear, non-stationary and low spatial resolution of EEG signals, traditional machine learning methods still have the disadvantages of low recognition rate and complicated feature extraction operations in EEG-based fatigue detection task. To tackle this problem, a fatigue detection method with transfer learning based on the Electrode-Frequency Distribution Maps (EFDMs) of EEG signals is proposed. A deep convolutional neural network is designed and pre-trained with SEED dataset, and then it is used for fatigue detection with transfer learning strategy. Experimental results show that the proposed convolutional neural network can automatically obtain vigilance related features from EFDMs, and achieve much better recognition results than traditional machine learning methods. Moreover, based on the transfer learning strategy, this model can also be used for other recognition tasks, which is helpful for promoting the application of EEG signals to the driver fatigue detection system.
- ElectroEncephaloGram (EEG),
- Fatigue detection,
- Transfer learning,
- Convolutional neural network,
- Electrode-frequency distribution maps

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References(23)

References

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