Fatigue Driving Vigilance Detection Using Convolutional Neural Networks and Scalp EEG Signals
Authors: 
Yue Fang, Chunxiao Han, Jing Liu, Fengjuan Guo, Yingmei Qin, Yanqiu CheAuthors Info & Claims
Yue Fang, Chunxiao Han, Jing Liu, Fengjuan Guo, Yingmei Qin, Yanqiu CheAuthors Info & ClaimsPages 137 - 142
Abstract
Fatigue driving is one of the important factors that cause traffic accidents. To solve this problem, this paper proposes a classification model based on the traditional convolutional neural network (CNN) to distinguish the vigilance state. First, the raw electroencephalogram (EEG) signals were converted into two-dimensional spectrograms by the short-time Fourier transform (STFT). Then, the CNN model was used for automatic features extraction and classification from these spectrograms. Finally, the performance of the trained CNN model was evaluated. The average of area under ROC Curve (AUC) was 1, the sensitivity was 91.4%, the average false prediction rate (FPR) was 0.02/h, and the accuracy rate was as high as 97%. The effectiveness of the CNN model was verified by the evaluation results.
References
[1]
Philip Pierre, Sagaspe Pastricia, and Moore Nicholas. 2005. Fatigue, Sleep Restriction and Driving Performance. Accident Analysis & Prevention 37, 3 (May 2005), 473-478. https://doi.org/10.1016/j.aap.2004.07.007
[2]
Brower K J.2003. The hazards and prevention of driving while sleepy. Sleep Medicine Reviews 7, 6 (December 2003). 507-521. https://doi.org/10.1016/S1087-0792(03)90004-9
[3]
Horne J A, and Burley C V. 2010. We know when we are sleepy: Subjective versus objective measurements of moderate sleepiness in healthy adults. Biological Psychology 83, 3 (March 2010), 266-268. https://doi.org/10.1016/j.biopsycho.2009.12.011
[4]
Axelsson J, Kecklund G, Akerstedt T, Donofrio P, Lekander M, and Ingre M. 2008.Sleepiness and performance in response to repeated sleep restriction and subsequent recovery during semi-laboratory conditions. Chronobiol Int 25, 2 (April 2008), 297-308. https://doi.org/ 10.1080/07420520802107031
[5]
Anuradha, Saha, Amit, Konar, Atulya K, and Nagar. 2017. EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers. IEEE Transactions on Emerging Topics in Computational Intelligence 1, 6 (December 2017), 437-453. https://doi.org/ 10.1109/TETCI.2017.2750761
[6]
Weisdorf S, J Duun‐Henriksen, and Kjeldsen M J. 2019. Ultra‐long‐term subcutaneous home monitoring of epilepsy—490days of EEG from nine patients. Epilepsia 60, 3 (September 2019). https://doi.org/10.1111/epi.16360
[7]
Lan, Zirui, Sourina Olga, Wang Lipo, Scherer Reinhold, and Muller-Putz. 2017. Unsupervised Feature Learning for EEG-based Emotion Recognition. 2017 International Conference on Cyberworlds (CW). IEEE Computer Society 1, 3 (September 2017), 182–185. https://doi.org/10.1109/CW.2017.19
[8]
Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, and Ziyue Xu. 2016. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging 35, 5 (May 2016), 1285-1298. https://doi.org/10.1109/TMI.2016.2528162
[9]
Shiu Kumar, Alok Sharma, Kabir Mamun, Tatsuhiko Tsunoda. 2016. A deep learning approach for motor imagery EEG signal classification. 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 34–39. https://doi.org/10.1109/APWC-on-CSE.2016.017
[10]
Al-Ayyoub M, Nuseir A, and Alsmearat K. 2018. Deep learning for Arabic NLP: A survey. J Comput Sci 26, 6 (November 2018), 522-531. https://doi.org/10.1016/j.jocs.2017.11.011
[11]
Abdelhamid Ossama, Deng Li, Yu Dong, Jiang Hui. 2013. Gradient-based learning applied to document recognition. IEEE 86,11(November 2013) 2278-2323. https://doi.org/10.1109/5.726791
[12]
Guo, Guanjun, Wang, Hanzi, and Zheng Jin. 2020. A Fast Face Detection Method via Convolutional Neural Network. Neurocomputing 395, 28 (June 2020), 128-137. https://doi.org/10.1016/j.neucom.2018.02.110
[13]
Murugan, P. 2018. Implementation of deep convolutional neural network in multi-class categorical image classification. Computer Vision and Pattern Recognition, 15 pages.
[14]
Weng W, Zhu X. 2021. INet: Convolutional Networks for Biomedical Image Segmentation. IEEE Access 9, (January 2021), 16591–16603. https://doi.org/10.1109/access.2021.3053408
[15]
Carneiro G, Oakden-Rayner L, Bradley A P. 2017. Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography. IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 130-134. https://doi.org/10.1109/ISBI.2017.7950485
[16]
Jia Liu, Maoguo Gong, Jiaojiao Zhao, Hao Li. 2016 Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft Computing 20, 12(September 2016), 4645-4657. https://doi.org/10.1109/ISBI.2017.7950485
[17]
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, Stefan CarlssonRazavian A S, Azizpour H, Sullivan J. 2014. CNN Features off-the-shelf: an Astounding Baseline for Recognition. 2014 IEEE conference on computer vision and pattern recognition workshops. IEEE, 512-519. https://doi.org/ 109/cvprw.2014.131
[18]
RT Schirrmeiste, JT Springenberg, LDJ Fiedere, M Glasstetter, K Eggensperger. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 38, 5(July 2017),5391-420. https://doi.org/10.1002/hbm.23730
[19]
Krizhevsky A, Sutskever I, Hinton G. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems 60, 6(June 2017), 84-90. https://doi.org/10.1145/3065386.
[20]
Paula Branco, Luis Torgo, and Rita P. Ribeiro. 2016. A survey of predictive modeling on imbalanced domains. ACM Comput. Surv 49, 2, Article 31 (August 2016), 50 pages. http://dx.doi.org/10.1145/2907070
[21]
Reuben Chip, Karoly Philippa, Freestone Dean R., and Temko Andriy; 2020. Ensembling crowdsourced seizure prediction algorithms using long‐term human intracranial EEG. Epilepsia 61, 2 (February 2020),7-12. https://doi.org/10.1111/epi.16418
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Published In
January 2022
250 pages
ISBN:9781450387408
DOI:10.1145/3517077
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Published: 22 May 2022
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- National Natural Science Foundation of China
- Natural Science Foundation of Tianjin
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ICMIP 2022
ICMIP 2022: 2022 7th International Conference on Multimedia and Image Processing
January 14 - 16, 2022
Tianjin, China
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