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Research Articles

Simplified Prediction Method for Detecting the Emergency Braking Intention Using EEG and a CNN Trained with a 2D Matrices Tensor Arrangement

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Pages 587-600 | Received 03 Jul 2021, Accepted 10 Feb 2022, Published online: 18 Apr 2022

References

  • Abdulkarim, H., & Al-Faiz, M. Z. (2021). Online multiclass EEG feature extraction and recognition using modified convolutional neural network method. International Journal of Electrical and Computer Engineering (IJECE), 11(5), 4016–4026. https://doi.org/10.11591/ijece.v11i5.pp4016-4026
  • Ahmadi, A., & Machiani, S. G. (2019). Drivers’ performance examination using a personalized adaptive curve speed warning: Driving simulator study. International Journal of Human–Computer Interaction, 35(11), 996–1007. https://doi.org/10.1080/10447318.2018.1561785
  • Aydarkhanov, R., Ušćumlić, M., Chavarriaga, R., Gheorghe, L., & del Millán, R. (2021). Closed-loop EEG study on visual recognition during driving. Journal of Neural Engineering, 18(2), 026010. https://doi.org/10.1088/1741-2552/abdfb2
  • Bisong, E. (2019). Google Colaboratory. In Building machine learning and deep learning models on Google cloud platform (pp. 59–64). Apress.
  • Bonyani, M., Rahmanian, M., & Jahangard, S. (2021). Predicting driver intention using deep neural network. arXiv:2105.14790.
  • Carneiro, T., Da Nóbrega, R. V. M., Nepomuceno, T., Bian, G.-B., De Albuquerque, V. H. C., & Reboucas Filho, P. P. (2018). Performance analysis of Google Colaboratory as a tool for accelerating Deep Learning applications. IEEE Access, 6, 61677–61685. https://doi.org/10.1109/ACCESS.2018.2874767
  • Chaabene, S., Bouaziz, B., Boudaya, A., Hökelmann, A., Ammar, A., & Chaari, L. (2021). Convolutional neural network for drowsiness detection using EEG signals. Sensors, 21(5), 1734. https://doi.org/10.3390/s21051734
  • Chen, S., Wang, Z., & Chen, W. (2020). Driver drowsiness estimation based on factorized bilinear feature fusion and a long-short-term recurrent convolutional network. Information, 12(1), 3. https://doi.org/10.3390/info12010003
  • Cui, N. (2018). Applying gradient descent in convolutional neural networks. Journal of Physics: Conference Series, 1004, 012027. https://doi.org/10.1088/1742-6596/1004/1/012027
  • Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
  • Diaz-Piedra, C., Sebastián, M. V., & Di Stasi, L. L. (2020). EEG theta power activity reflects workload among army combat drivers: An experimental study. Brain Sciences, 10(4), 199. https://doi.org/10.3390/brainsci10040199
  • Diederichs, F., Schüttke, T., Spath, D. (2015). Driver intention algorithm for pedestrian protection and automated emergency braking systems. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems (pp. 1049–1054). IEEE.
  • Dindorf, R., Takosoglu, J., & Wos, P. (2021). Study on a brain-controlled pneumatic actuator to assist emergency braking of a vehicle. Communications - Scientific Letters of the University of Zilina, 23(3), F49–F57. https://doi.org/10.26552/com.C.2021.3.F49-F57
  • Faraji, F., Lotfi, F., Khorramdel, J., Najafi, A., Ghaffari, A. (2021). Drowsiness detection based on driver temporal behavior using a new developed dataset. https://arxiv.org/abs/2104.00125
  • Gaurav, G., Anand, R., & Kumar, V. (2021). EEG based cognitive task classification using multifractal detrended fluctuation analysis. Cognitive Neurodynamics, 15(6), 999–1013. https://doi.org/10.1007/s11571-021-09684-z
  • Google. (2018). Colaboratory. Frequently asked questions. https://research.google.com/colaboratory/faq.html
  • Gougeh, R. A., Rezaii, T. Y., & Farzamnia, A. (2021). An automatic driver assistant based on intention detecting using EEG signal. In Z. Md Zain. (Eds.), Proceedings of the 11th National technical seminar on unmanned system technology 2019 (pp. 617–627). Springer Singapore.
  • Haufe, S., Kim, J.-W., Kim, I.-H., Sonnleitner, A., Schrauf, M., Curio, G., & Blankertz, B. (2014). Electrophysiology-based detection of emergency braking intention in real-world driving. Journal of Neural Engineering, 11(5), 056011. https://doi.org/10.1088/1741-2560/11/5/056011
  • Haufe, S., Treder, M. S., Gugler, M. F., Sagebaum, M., Curio, G., & Blankertz, B. (2011). EEG potentials predict upcoming emergency brakings during simulated driving. Journal of Neural Engineering, 8(5), 056001. https://doi.org/10.1088/1741-2560/8/5/056001
  • Hernández, L. G., Mozos, O. M., Ferrández, J. M., & Antelis, J. M. (2018). EEG-based detection of braking intention under different car driving conditions. Frontiers in Neuroinformatics, 12, 29. https://doi.org/10.3389/fninf.2018.00029
  • Hong, J.-W., Wang, Y., & Lanz, P. (2020). Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. International Journal of Human–Computer Interaction, 36(18), 1768–1774. https://doi.org/10.1080/10447318.2020.1785693
  • Houshmand, S., Kazemi, R., Salmanzadeh, H. (2021). A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 235(9). https://doi.org/10.1177/09544119211017813
  • Ji, H., Alfarraj, O., & Tolba, A. (2020). Artificial intelligence-empowered edge of vehicles: Architecture, enabling technologies, and applications. IEEE Access, 8, 61020–61034. https://doi.org/10.1109/ACCESS.2020.2983609
  • Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., & Bajwa, R. (2017). In-datacenter performance analysis of a tensor processing unit [Paper presentation]. Proceedings of the 44th Annual International Symposium on Computer Architecture, Toronto, Canada (pp. 1–12). https://doi.org/10.1145/3079856.3080246
  • Ju, J., & Bi, L. (2020). Driving intention decoding from EMG signals for human-vehicle interaction [Paper presentation]. 2020 IEEE International Conference on Real-Time Computing and Robotics (RCAR) (pp. 286–290). https://doi.org/10.1109/RCAR49640.2020.9303311
  • Kaczorowska, M., Plechawska-Wójcik, M., & Tokovarov, M. (2021). Interpretable machine learning models for three-way classification of cognitive workload levels for eye-tracking features. Brain Sciences, 11(2), 210. https://doi.org/10.3390/brainsci11020210
  • Kamzanova, A., Matthews, G., & Kustubayeva, A. (2020). EEG coherence metrics for vigilance: Sensitivity to workload, time-on-task, and individual differences. Applied Psychophysiology and Biofeedback, 45(3), 183–194. https://doi.org/10.1007/s10484-020-09461-4
  • Karthaus, M., Wascher, E., & Getzmann, S. (2021). Distraction in the driving simulator: An event-related potential (ERP) study with young, middle-aged, and older drivers. Safety, 7(2), 36. https://doi.org/10.3390/safety7020036
  • Kim, I.-H., Kim, J.-W., Haufe, S., & Lee, S.-W. (2015). Detection of braking intention in diverse situations during simulated driving based on EEG feature combination. Journal of Neural Engineering, 12(1). https://doi.org/10.1088/1741-2560/12/1/016001
  • Kumar, A., Vashishtha, G., Gandhi, C., Zhou, Y., Glowacz, A., & Xiang, J. (2021). Novel convolutional neural network (NCNN) for the diagnosis of bearing defects in rotary machinery. IEEE Transactions on Instrumentation and Measurement, 70, 1–10. https://doi.org/10.1109/TIM.2021.3055802
  • Kumar, S., Wang, Y., Young, C., Bradbury, J., Kumar, N., & Chen, D. (2021). Exploring the limits of concurrency in ML training on Google TPUs. Proceedings of Machine Learning and Systems, 3, 1–4. https://proceedings.mlsys.org/paper/2021.
  • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
  • Liu, C. C., Hosking, S. G., & Lenné, M. G. (2009). Predicting driver drowsiness using vehicle measures: Recent insights and future challenges. Journal of Safety Research, 40(4), 239–245. https://doi.org/10.1016/j.jsr.2009.04.005
  • Lu, T., Marin, T., Zhuo, Y., Chen, Y.-F., & Ma, C. (2020). Accelerating MRI reconstruction on TPUs [Paper presentation]. 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1–9). https://doi.org/10.1109/HPEC43674.2020.9286192
  • Mandhouj, B., Cherni, M. A., & Sayadi, M. (2021). An automated classification of EEG signals based on spectrogram and CNN for epilepsy diagnosis. Analog Integrated Circuits and Signal Processing, 108(1), 1–10.
  • Monteiro, T. G., Skourup, C., & Zhang, H. (2021). A task agnostic mental fatigue assessment approach based on EEG frequency bands for demanding maritime operation. IEEE Instrumentation & Measurement Magazine, 24(4), 82–88. https://doi.org/10.1109/MIM.2021.9448258
  • National Highway Traffic Safety Administration. (2003). Automotive collision avoidance system field operational test. Technical report, U.S. Department of Transportation. https://www.nhtsa.gov.
  • Nielsen, M. A. (2015). Neural networks and deep learning (Vol. 2018). Determination Press. http://neuralnetworksanddeeplearning.com
  • Nisar, S., Khan, O. U., & Tariq, M. (2016). An efficient adaptive window size selection method for improving spectrogram visualization. Computational Intelligence and Neuroscience, 2016, 6172453. https://doi.org/10.1155/2016/6172453
  • Palaniappan, R., Mouli, S., Fringi, E., Bowman, H., & Mcloughlin, I. (2021). Incandescent bulb and LED brake lights: Novel analysis of reaction times. IEEE Access, 9, 29143–29152. https://doi.org/10.1109/ACCESS.2021.3058579
  • Pang, L., Guo, L., Zhang, J., Wanyan, X., Qu, H., & Wang, X. (2021). Subject-specific mental workload classification using EEG and stochastic configuration network (SCN). Biomedical Signal Processing and Control, 68, 102711. https://doi.org/10.1016/j.bspc.2021.102711
  • Python. (2019). Python Software Foundation. Python Language Reference. Version 3.7. https://www.python.org
  • Ren, Y., & Wu, Y. (2014). Convolutional deep belief networks for feature extraction of EEG signal [Paper presentation]. 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 2850–2853). https://doi.org/10.1109/IJCNN.2014.6889383
  • Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11), 5391–5420. https://doi.org/10.1002/hbm.23730
  • Sernageomin. (2018). Accidentabilidad Minera, Gobierno de Chile. Servicio Nacional de Geología y Minería. Seguridad minera. https://www.sernageomin.cl/mineria.
  • Sharbrough, F. (1991). American Electroencephalographic Society guidelines for standard electrode position nomenclature. Journal of Clinical Neurophysiology, 8, 200–202. https://ci.nii.ac.jp/naid/1000918324655.
  • Silva, F. P. d. (2014). Mental workload, task demand and driving performance: What relation. Procedia - Social and Behavioral Sciences, 162, 310–319. https://doi.org/10.1016/j.sbspro.2014.12.212
  • Society of Automotive Engineers. (2021). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (Tech. Rep.). SAE International. https://www.sae.org/standards/content/j3016-202104
  • Song, R. (2021). Driver intention prediction using model-added Bayesian network. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(5), 1236–1244. https://doi.org/10.1177/0954407020968967
  • Tabar, Y. R., & Halici, U. (2017). A novel deep learning approach for classification of EEG motor imagery signals. Journal of Neural Engineering, 14(1), 016003.
  • Van Benthem, K., Cebulski, S., Herdman, C. M., & Keillor, J. (2018). An EEG brain–computer interface approach for classifying vigilance states in humans: A gamma band focus supports low misclassification rates. International Journal of Human–Computer Interaction, 34(3), 226–237. https://doi.org/10.1080/10447318.2017.1342942
  • Vidal, M., Rosso, M., Aguilera, A. M. (2021). P-spline smoothed functional ICA of EEG data. arXiv preprint arXiv:2101.05769. https://arxiv.org/abs/2101.05769
  • Vizcaya Cardenas, R., Flores Albino, J., Landassuri Moreno, V., & Lazcano Salas, S. (2017). Desempeño de una Red Neuronal Convolucional para Clasificación de Señales de Tránsito. Universidad Autónoma de México. Repositorio Instirucional (2017).
  • Wang, S., Zhao, X., Yu, Q., & Yuan, T. (2020). Identification of driver braking intention based on long short-term memory (LSTM) network. IEEE Access., 8, 180422–180432. https://doi.org/10.1109/ACCESS.2020.3027811
  • Wang, X.-Y., Liu, Y-Q., Xu, Q., Xia, Y-Y., Zhao, H-X., Liu, S-J., & Han, J-Y. (2019). Feature extraction and dynamic identification of driving intention adapting to multi-mode emotions. Advances in Mechanical Engineering, 11(4), 168781401983990. https://doi.org/10.1177/1687814019839906
  • Xiong, L., Teng, G., Yu, Z., Zhang, W., & Feng, Y. (2016). Novel stability control strategy for distributed drive electric vehicle based on driver operation intention. International Journal of Automotive Technology, 17(4), 651–663. https://doi.org/10.1007/s12239-016-0064-3
  • Yazdanbakhsh, A., Seshadri, K., Akin, B., Laudon, J., Narayanaswami, R. (2021). An evaluation of edge TPU accelerators for convolutional neural networks. arXiv preprint arXiv:2102.10423. https://arxiv.org/abs/2102.10423
  • Zhang, X. (2021). Driver mental states detection during highly automated driving by decoding brain signals [Doctoral dissertation]. https://www.depositonce.tu-berlin.de/handle/11303/12787
  • Zhang, X., Yan, X., Stylli, J., & Platt, M. L. (2021). Exploring the effects of EEG signals on collision cases happening in the process of young drivers’ braking. Transportation Research Part F: Traffic Psychology and Behaviour, 80, 381–398. https://doi.org/10.1016/j.trf.2021.05.010
  • Zhang, Y., & Kumada, T. (2017). Relationship between workload and mind-wandering in simulated driving. PlOS One, 12(5), e0176962. https://doi.org/10.1371/journal.pone.0176962
  • Zuojin, L., Li, S. E., Li, R., Cheng, B., & Shi, J. (2017). Online detection of driver fatigue using steering wheel angles for real driving conditions. Sensors, 17(3), 495. https://doi.org/10.3390/s17030495

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