Comparison of Machine Learning Algorithms for Recognizing Drowsiness in Drivers using Electroencephalogram (EEG) Signals

Ruya Akinci; Erhan Akdogan; Mehmet Emin Aktan

doi:10.18201/ijisae.2022.266

Authors

Ruya Akinci Yıldız Technical University https://orcid.org/0000-0001-6272-1178
Erhan Akdogan Yıldız Technical University https://orcid.org/0000-0003-1223-2725
Mehmet Emin Aktan Bartın University https://orcid.org/0000-0001-7058-8362

DOI:

https://doi.org/10.18201/ijisae.2022.266

Keywords:

Alpha band, drowsiness, Electroencephalogram, machine learning

Abstract

Drowsiness is one of the major reasons that causes traffic accidents. Thus, its early detection can help preventing accidents by warning the drivers before the unfortunate events. This study focuses on the detection of drowsiness using classification of alpha waves from EEG signals with 25 different machine learning algorithms. The results were evaluated in terms of classification accuracy and classification time. Accordingly, the Bagged Trees and Subspace KNN models gave better results in terms of classification accuracy compared to the Tree algorithm methodology, although the classification times are relatively high. Tree Algorithms approach displays optimal features as it serves as both a considerably satisfactory classification accuracy in much shorter times. The requirements in terms of accuracy and time for the recognition of drowsiness should determine the method to be applied.

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References

[1] B. C. Tefft, “Prevalence of motor vehicle crashes involving drowsy drivers”, United States, 2009-2013. Washington, DC: AAA Foundation for Traffic Safety, 2014.
[2] Y. Dong, Z. Hu, K. Uchimura, N. Murayama, “Driver Inattention Monitoring System for Intelligent Vehicles: A Review”, IEEE Transactions on Intelligent Transportation Systems, 12(2): 596–614, 2011.
[3] J. N. Mindoro, C. D. Casuat, A. S. Alon, M. A. F. Malbog, & J. A. B. Susa, “Drowsy or Not? Early Drowsiness Detection utilizing Arduino Based on Electroencephalogram (EEG) Neuro-Signal”, International Journal, 9(2), 2020.
[4] A. R. Sparrow, C. M. LaJambe, & H. P. Van Dongen, “Drowsiness measures for commercial motor vehicle operations.” Accident Analysis & Prevention, 126, 146-159, 2019.
[5] M. Jagannath, V. Balasubramanian, “Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator.” Applied ergonomics. 45(4):1140–7. pmid:24581559, 2014.
[6] Y. Liang, W. J. Horrey, M. E. Howard, M. L. Lee, C. Anderson, M. S. Shreeve, ... & C. A. Czeisler, “Prediction of drowsiness events in night shift workers during morning driving.” Accident Analysis & Prevention, 126, 105-114, 2019.
[7] S. Chinara, “Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal”, Journal of neuroscience methods, 347, 108927, 2021.
[8] A. Sahayadhas, K. Sundaraj, & M. Murugappan, “Detecting driver drowsiness based on sensors: a review.” Sensors (Basel, Switzerland), 12(12), 16937–16953, 2012.
[9] C.C. Liu, S.G. Hosking, M.G. Lenné, “Predicting driver drowsiness using vehicle measures: recent insights and future challenges.” J Safety Res. 40(4):239-245, 2009.
[10] Y. Ming, D. Wu, Y. K. Wang, Y. Shi, & C. T. Lin, “EEG-based drowsiness estimation for driving safety using deep Q-learning.” IEEE Transactions on Emerging Topics in Computational Intelligence, 2020.
[11] M. Mohanty, R. Sikka, “Android application to detect drowsiness during driving vehicle.” Materials Today: Proceedings, 2021.
[12] J. Jo, S.J. Lee, K.R. Park, I.J. Kim, J. Kim, “Detecting driver drowsiness using feature-level fusion and user-specific classification.” Expert Systems with Applications, 41(4):1139–52, 2014.
[13] L. Wang, D. Johnson, Y. Lin, “Using EEG to detect driving fatigue based on common spatial pattern and support vector machine.”, Turkish Journal of Electrical Engineering and Computer Sciences, 29: 1429–1444, 2021.
[14] H. Jianfeng, M. Zhendong, W. Ping, “Multi-feature authentication system based on event evoked electroencephalogram.” Journal of Medical Imaging and Health Informatics, 5(4):862–70, 2015.
[15] V. P. Balam, & S. Chinara, “Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection.” Physical and Engineering Sciences in Medicine, 1-14, 2021.
[16] A. G. Correa, L. Orosco, & E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis.” Medical engineering & physics, 36(2), 244-249, 2014.
[17] S. Kar, M. Bhagat, A. Routray, “EEG signal analysis for the assessment and quantification of driver’s fatigue.” Transportation research part F: traffic psychology and behaviour. 13(5):297–306, 2010.
[18] H. Wang, L. Zhang, & L. Yao, “Application of genetic algorithm based support vector machine in selection of new EEG rhythms for drowsiness detection.” Expert Systems with Applications, 171, 114634, 2021.
[19] J. Min, C. Xiong, Y. Zhang, & M. Cai, “Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model.” Biomedical Signal Processing and Control, 69, 102857, 2021.
[20] S. Chaabene, B. Bouaziz, A. Boudaya, A. Hökelmann, A. Ammar, L. Chaari, “Convolutional Neural Network for Drowsiness Detection Using EEG Signals.” Sensors (Basel), 21(5):1734,2021.
[21] Z. Ren, R. Li, B. Chen, H. Zhang, Y. Ma, C. Wang, ... & Y. Zhang, “EEG-based driving fatigue detection using a two-level learning hierarchy radial basis function.” Frontiers in Neurorobotics, 15, 2021.
[22] F. Rundo, S. Rinella, S. Massimino, M. Coco, G. Fallica, R. Parenti, S. Conoci, V. Perciavalle, “An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal.” Computation, 7, 13, 2019.
[23] C. Grégoire, L. C. Pedro, “Rodrigues, & Marco Congedo.” EEG Alpha Waves dataset [Data set], Zenodo, https://doi.org/10.5281/zenodo.2348892, 2018.
[24] R. D. Ogilvie, “The process of falling asleep,” Sleep Medicine Reviews, 5(3): 247-270, 2001.
[25] N. Kerkeni, F. Alexandre, M.H. Bedoui, L. Bougrain, & M. Dogui, “Automatic classification of sleep stages on a EEG signal by artificial neural networks.” , In 5th WSEAS International Conference on SIGNAL, SPEECH and IMAGE PROCESSING-WSEAS SSIP'05, 2005.
[26] A. J. Basha, B. S. Balaji, S. Poornima, M. Prathilothamai, & K. Venkatachalam, “Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel.” Journal of Ambient Intelligence and Humanized Computing, 12(6), 6189-6197, 2021.
[27] Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby, V. Delannoy, ... & A. Lécuyer, “Openvibe: An open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments.” Presence, 19(1), 35-53, 2010.
[28] C. Arrouët, M. Congedo, J. E. Marvie, F. Lamarche, A. Lécuyer, & B. Arnaldi, “Open-ViBE: a three dimensional platform for real-time neuroscience.” Journal of Neurotherapy, 9(1), 3-25, 2005.
[29] N. A. Alzahab, M. Baldi, & L. Scalise, “Efficient feature selection for electroencephalogram-based authentication.”, IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6), IEEE, 2021.
[30] P. A. Abhang, B. W. Gawali, & S. C. Mehrotra, “Technical aspects of brain rhythms and speech parameters.” Introduction to EEG-and Speech-Based Emotion Recognition, 51-79, 2016.
[31] A. Tharwat, “Classification assessment methods. Applied Computing and Informatics.” Applied Computing and Informatics,2020.
[32] J. Sujatha, & S. P. Rajagopalan, “Performance evaluation of machine learning algorithms in the classification of Parkinson disease using voice attributes.” International Journal of Applied Engineering Research, 12(21), 10669-10675, 2017.