A Review on Deep Learning Techniques for EEG-Based Driver Drowsiness detection systems
DOI:
https://doi.org/10.31449/inf.v48i3.5056Abstract
Driver Drowsiness is considered one of the significant causes of road accidents and fatal injuries. Due to this, creating systems that can monitor drivers and detect early drowsiness has become an important field of research and a challenging task in recent years. Several research attempts were proposed to solve this problem based on several approaches and techniques. The Electroencephalogram (EEG) is one of the most efficient and reliable method, among the physiological signals-based monitoring approaches. In this area, many Machine Learning (ML) techniques have been used to detect EEG-based driver drowsiness. However, due to the limitations of ML techniques, many researchers have shifted their focus to the use of deep learning (DL) techniques, which have demonstrated superior performance in many fields including the physiological signals classification tasks. In this paper, we review and discuss numerous new research papers that been proposed and implemented drivers’ drowsiness detection systems based on EEG and deep learning techniques. In addition, we have outlined the limitations and difficulties of the existing works and proposed some propositions that will enhance and generalize the results.References
Majumder, S., Guragain, B., Wang, C., & Wilson, N (2019, May) On-board drowsiness detection using EEG: Current status and future prospects. In 2019 IEEE International Conference on Electro Information Technology (EIT) (pp. 483-490). IEEE
Dutta, A., Kour, S., & Taran, S (2020) Automatic drowsiness detection using electroencephalogram signal. Electronics Letters, 56(25), 1383-1386
Chaabene, S.; Bouaziz, B.; Boudaya, A.; Hökelmann, A.; Ammar,A.; Chaari, L (2021) Convolutional Neural Network for Drowsiness Detection Using EEG Signals. Sensors 2021, 21,1734
Covington, T (2021, September 30) Drowsy Driving Statistics. THE zebra. https://www.thezebra.com/resources/research/drowsy-driving-statistics/?__cf_chl_captcha_tk__=hzpmyRUeMjxexnLGFixSOgDVfrH_5vqfUbB0LwVfJIM-1637260303-0-gaNycGzNBiU#statistics-2020 . Accessed March 2022
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, 09544119211017813
Balam, V. P., Sameer, V. U., & Chinara, S (2021) Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram. IET Intelligent Transport Systems, 15(4), 514-524
Shalash, W. M (2019, December) Driver fatigue detection with single EEG channel using transfer learning. In 2019 IEEE International Conference on Imaging Systems and Techniques (IST) (pp. 1-6). IEEE
Doma, V., & Pirouz, M (2020) A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals. Journal of Big Data, 7(1), 1-21
Gwak, J., Shino, M., & Hirao, A (2018, November) Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 1794-1800). IEEE
Hu, J., & Min, J (2018) Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model. Cognitive neurodynamics, 12(4), 431-440
Mu, Z., Hu, J., & Yin, J (2017) Driving fatigue detecting based on EEG signals of forehead area. International Journal of Pattern Recognition and Artificial Intelligence, 31(05), 1750011
Ma, Y., Zhang, S., Qi, D., Luo, Z., Li, R., Potter, T., & Zhang, Y (2020) Driving drowsiness detection with EEG using a modified hierarchical extreme learning machine algorithm with particle swarm optimization: A pilot study. Electronics, 9(5), 775.
Zeng, H., Yang, C., Dai, G., Qin, F., Zhang, J., & Kong, W (2018) EEG classification of driver mental states by deep learning. Cognitive neurodynamics, 12(6), 597-606
Gao, Z., Wang, X., Yang, Y., Mu, C., Cai, Q., Dang, W., & Zuo, S (2019) EEG-based spatio–temporal convolutional neural network for driver fatigue evaluation. IEEE transactions on neural networks and learning systems, 30(9), 2755-2763
Ding, S., Yuan, Z., An, P., Xue, G., Sun, W., & Zhao, J (2019, November) Cascaded convolutional neural network with attention mechanism for mobile eeg-based driver drowsiness detection system. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1457-1464). IEEE
Zhu, M., Chen, J., Li, H., Liang, F., Han, L., & Zhang, Z (2021) Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network. Neural computing and applications, 1-16
Lin, Z., Qiu, T., Liu, P., Zhang, L., Zhang, S., & Mu, Z. (2021). Fatigue driving recognition based on deep learning and graph neural network. Biomedical Signal Processing and Control, 68, 102598.
Cheng, E. J., Young, K. Y., & Lin, C. T (2018, November) Image-based EEG signal processing for driving fatigue prediction. In 2018 International Automatic Control Conference (CACS) (pp. 1-5). IEEE
Budak, U., Bajaj, V., Akbulut, Y., Atila, O., & Sengur, A (2019) An effective hybrid model for EEG-based drowsiness detection. IEEE sensors journal, 19(17), 7624-7631
Majumder, S., Guragain, B., Wang, C., & Wilson, N (2019, May) On-board drowsiness detection using EEG: Current status and future prospects. In 2019 IEEE International Conference on Electro Information Technology (EIT) (pp. 483-490). IEEE
Ma, J., Gu, J., Jia, H., Yao, Z., & Chang, R (2018) The relationship between drivers’ cognitive fatigue and speed variability during monotonous daytime driving. Frontiers in psychology, 9, 459
Im C-H, I. (2018). Computational EEG Analysis: Methods and Applications. Im C.-H., editor.
Shoeibi, A., Khodatars, M., Ghassemi, N., Jafari, M., Moridian, P., Alizadehsani, R., ... & Acharya, U. R (2021) Epileptic Seizures Detection Using Deep Learning Techniques: A Review. International Journal of Environmental Research and Public Health, 18(11), 5780
Houssam M (28 septembre 2020) Deep Learning : What is it?. DataScientest. https://datascientest.com/deep-learning-definition Accessed March 2021
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., ... & Farhan, L (2021) Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8(1), 1-74
Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M (2021) Deep learning for motor imagery EEG-based classification: A review. Biomedical Signal Processing and Control, 63, 102172
Sarker, I. H (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 1-20
Mao, W. L., Fathurrahman, H. I. K., Lee, Y., & Chang, T. W (2020) EEG dataset classification using CNN method. In Journal of physics: conference series (Vol. 1456, No. 1, p. 012017). IOP Publishing
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J (2021) 1D convolutional neural networks and applications: A survey. Mechanical systems and signal processing, 151, 107398
Apostolopoulos, I. D., & Mpesiana, T. A (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640
Hussain, M., Bird, J. J., & Faria, D. R (2018, September) A study on cnn transfer learning for image classification. In UK Workshop on computational Intelligence (pp. 191-202). Springer, Cham
Khessiba, S., Blaiech, A. G., Khalifa, K. B., Abdallah, A. B., & Bedoui, M. H (2020) Innovative deep learning models for EEG-based vigilance detection. Neural Computing and Applications, 1-17
Aleia Knight (2021, April) LSTM Neural Network: The Basic Concept. Towards data science. https://towardsdatascience.com/lstm-neural-network-the-basic-concept-a9ba225616f7 Accessed March 2021
Jason Brownlee (May, 2017) A Gentle Introduction to Long Short-Term Memory Networks by the Experts. Machine learning mastery. https://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/ Accessed March 2021
Ambika Choudhury (October, 2020) Gated Recurrent Unit – What Is It And How To Learn. Analytics in diamag. https://analyticsindiamag.com/gated-recurrent-unit-what-is-it-and-how-to-learn/ Accessed March 2021
B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé (2000) Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220
Min, Jianliang; Wang, Ping; Hu, Jianfeng (2017) The original EEG data for driver fatigue detection. figshare. Dataset. https://doi.org/10.6084/m9.figshare.5202739.v1 Accessed March 2021
Cao, Zehong; Chuang, Michael; King, J.T.; Lin, Chin-Teng (2019) Multi-channel EEG recordings during a sustained-attention driving task (pre-processed dataset). figshare. Dataset. https://doi.org/10.6084/m9.figshare.7666055.v3 Accessed March 2021
Y. Ichimaru and G. B. Moody (1999) Development of the Polysomnographic database on CD-ROM,” Psychiatry Clin. Neurosci., vol. 53, no. 2, pp. 175–177, Apr. 1999
Zheng, W. L., & Lu, B. L (2017) A multimodal approach to estimating vigilance using EEG and forehead EOG. Journal of neural engineering, 14(2), 026017
J. Min, P. Wang, and J. Hu 2017 Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system,” PLoS One, vol. 12, no. 12, Dec. 2017
Ko, W., Oh, K., Jeon, E., & Suk, H. I (2020, February) Vignet: A deep convolutional neural network for eeg-based driver vigilance estimation. In 2020 8th International Winter Conference on Brain-Computer Interface (BCI) (pp. 1-3). IEEE
Guarda, L., Astorga, N., Droguett, E., Moura, M., & Ramos, M (2018) Drowsiness detection using electroencephalography signals: A deep learning based model. Proceedings of the Probabilistic Safety Assessment and Management PSAM, Los Angeles, CA, USA, 16
Lee, C., Choi, R. H., & An, J (2021, February) Deep Neural Network for Drowsiness Detection from EEG. In 2021 9th International Winter Conference on Brain-Computer Interface (BCI) (pp. 1-3). IEEE
Turkoglu, M., Alcin, O. F., Aslan, M., Al-Zebari, A., & Sengur, A (2021) Deep rhythm and long short term memory-based drowsiness detection. Biomedical Signal Processing and Control, 65, 102364
Chen, J., Wang, S., He, E., Wang, H., & Wang, L (2021) Recognizing drowsiness in young men during real driving based on electroencephalography using an end-to-end deep learning approach. Biomedical Signal Processing and Control, 69, 102792
Gao, Z. K., Li, Y. L., Yang, Y. X., & Ma, C (2019) A recurrence network-based convolutional neural network for fatigue driving detection from EEG. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(11), 113126
Zeng, H., Yang, C., Dai, G., Qin, F., Zhang, J., & Kong, W (2018) EEG classification of driver mental states by deep learning. Cognitive neurodynamics, 12(6), 597-606
Jeong, J. H., Yu, B. W., Lee, D. H., & Lee, S. W (2019) Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional LSTM network using electroencephalography signals. Brain sciences, 9(12), 348
Michielli, N., Acharya, U. R., & Molinari, F (2019) Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Computers in biology and medicine, 106, 71-81
Latreche, I., Slatnia, S., & Kazar, O. (2022, October). CNN-LSTM To Identify The Most Informative EEG-Based Driver Drowsiness Detection Brain Region. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 725-730). IEEE.
H. Shabani, M. Mikaili, and S. M. R. Noori (2016) Assessment of recurrence quantification analysis (rqa) of eeg for development of a novel drowsiness detection system. Biomed. Eng. Lett., Vol. 6, no. 3, pp. 196–204.
A. Jalilifard, and E. B. Pizzolato (2016) An efficient k-nn approach for automatic drowsiness detection using singlechannel eeg recording. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp.820–824.
G. Li, andW.-Y. Chung (2015) A contextaware eeg headset system for early detection of driver drowsiness. Sensors, Vol. 15, no. 8, pp. 20873–20893.
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