Analysis of Driver Drowsiness Detection using EEG and EOG

  • Authors

    • Omprakash K Firke
    • Dr Manish Jain
    2018-04-15
    https://doi.org/10.14419/ijet.v7i2.17.11557
  • EEG, EOG, ECG, Driver Drowsiness, OSS Criteria.

  • This paper propose here a resolutely oriented approach to the study of the driver in order to detect the driver drowsiness starting from physiological information (related to the brain activity) and video (related to the ocular activity). The goal of this work is to develop a system for automatic detection of driver drowsiness in the driver from electroencephalographic (EEG) (describing brain activity) and video of the driver. This approach is motivated by the fact that driver drowsiness physicians mainly work from brain and visual data to detect driver drowsiness. In addition, the complementarity of brain and ocular activities seems to indicate that the contribution of cerebral information would improve the reliability of the camera-based approaches (thus using only the visual cues) used for the automatic detection of the decline of vigilance.

     

  • References

    1. [1] Lee, M. and Kim, J., Kwungpook National University Industry-Academic Cooperation Foundation, 2017. Vehicle control system for providing warning message and method thereof. U.S. Patent 9,566,909.
      [2] Oh, J., Washington, S.P. and Nam, D., 2006. Accident prediction model for railway-highway interfaces. Accident Analysis & Prevention, 38(2), pp.346-356.
      [3] Sahayadhas, A., Sundaraj, K. and Murugappan, M., 2012. Detecting driver drowsiness based on sensors: a review. Sensors, 12(12), pp.16937-16953.
      [4] Anderer, P., Gruber, G., Parapatics, S., Woertz, M., Miazhynskaia, T., Kl?sch, G., Saletu, B., Zeitlhofer, J., Barbanoj, M.J., Danker-Hopfe, H. and Himanen, S.L., 2005. An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24ï¿— 7 utilizing the Siesta database. Neuropsychobiology, 51(3), pp.115-133.
      [5] Van Dongen, H., Maislin, G., Mullington, J.M. and Dinges, D.F., 2003. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep, 26(2), pp.117-126.
      [6] Renner, G. and Mehring, S., 1997. Lane departure and drowsiness-two major accident causes-one safety system. In MOBILITY FOR EVERYONE. 4TH WORLD CONGRESS ON INTELLIGENT TRANSPORT SYSTEMS, 21-24 OCTOBER 1997, BERLIN.(PAPER NO. 2264).
      [7] Hu, S. and Zheng, G., 2009. Driver drowsiness detection with eyelid related parameters by Support Vector Machine. Expert Systems with Applications, 36(4), pp.7651-7658.
      [8] Rogï¿©, J., Pï¿©bayle, T., Lambilliotte, E., Spitzenstetter, F., Giselbrecht, D. and Muzet, A., 2004. Influence of age, speed and duration of monotonous driving task in traffic on the driver's useful visual field. Vision research, 44(23), pp.2737-2744.
      [9] K?thner, I., Wriessnegger, S.C., Mller-Putz, G.R., Kbler, A. and Halder, S., 2014. Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface. Biological psychology, 102, pp.118-129.
      [10] Bianchi-Berthouze, N., 2013. Understanding the role of body movement in player engagement. Human-Computer Interaction, 28(1), pp.40-75.
      [11] Cahn, B.R. and Polich, J., 2013. Meditation states and traits: EEG, ERP, and neuroimaging studies.
      [12] Wong, C.W., Olafsson, V., Tal, O. and Liu, T.T., 2013. The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures. Neuroimage, 83, pp.983-990.
      [13] Gola, M., Magnuski, M., Szumska, I. and Wr?bel, A., 2013. EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. International Journal of Psychophysiology, 89(3), pp.334-341.
      [14] Laurent, F., Valderrama, M., Besserve, M., Guillard, M., Lachaux, J.P., Martinerie, J. and Florence, G., 2013. Multimodal information improves the rapid detection of mental fatigue. Biomedical Signal Processing and Control, 8(4), pp.400-408.
      [15] Lim, J., Quevenco, F.C. and Kwok, K., 2013. EEG alpha activity is associated with individual differences in post-break improvement. Neuroimage, 76, pp.81-89.
      [16] Zarjam, P., Epps, J., Chen, F. and Lovell, N.H., 2013. Estimating cognitive workload using wavelet entropy-based features during an arithmetic task. Computers in biology and medicine, 43(12), pp.2186-2195.
      [17] Kortelainen, J., Jia, X., Sepp?nen, T. and Thakor, N., 2012. Increased electroencephalographic gamma activity reveals awakening from isofluraneanaesthesia in rats. British journal of anaesthesia, 109(5), pp.782-789.
      [18] Chen, L.L., Zhao, Y., Zhang, J. and Zou, J.Z., 2015. Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Systems with Applications, 42(21), pp.7344-7355.
      [19] ?kerstedt, T., Hallvig, D., Anund, A., Fors, C., Schwarz, J. and Kecklund, G., 2013. Having to stop driving at night because of dangerous sleepiness-awareness, physiology and behaviour. Journal of sleep research, 22(4), pp.380-388.
      [20] Anderer, P., 2015. Advanced analysis of pharmaco-sleep data in humans. Neuropsychobiology, 72(3-4), pp.178-187.
      [21] Lawoyin, S., 2014. Novel technologies for the detection and mitigation of drowsy driving. Virginia Commonwealth University.
      [22] Nguyen, Q.N., Tho, L.T.A., Van, T.V., Yu, H. and Thang, N.D., 2017, June. Visual based drowsiness detection using facial features. In International Conference on the Development of Biomedical Engineering in Vietnam (pp. 723-727). Springer, Singapore.
      [23] Mallet, Y., Coomans, D., Kautsky, J. and De Vel, O., 1997. Classification using adaptive wavelets for feature extraction. IEEE transactions on pattern analysis and machine intelligence, 19(10), pp.1058-1066.
      [24] daSilveira, T.L., Kozakevicius, A.J. and Rodrigues, C.R., 2016. Automated drowsiness detection through wavelet packet analysis of a single EEG channel. Expert Systems with Applications, 55, pp.559-565.
      [25] Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A. and Ahmad, I., 2015. A review of channel selection algorithms for EEG signal processing. EURASIP Journal on Advances in Signal Processing, 2015(1), p.66.
      [26] Dabbu, S., Malini, M., Reddy, B.R. and Vyza, Y.S.R., 2017. ANN based Joint Time and frequency analysis of EEG for detection of driver drowsiness. Defence Life Science Journal, 2(4), pp.406-415.
      [27] Youh, J., Hong, J.S., Han, D.H., Chung, U.S., Min, K.J., Lee, Y.S. and Kim, S.M., 2017. Comparison of electroencephalography (EEG) coherence between major depressive disorder (MDD) without comorbidity and MDD comorbid with Internet gaming disorder. Journal of Korean medical science, 32(7), pp.1160-1165.
      [28] Piotrowski, Z. and Szypulska, M., 2017. Classification of falling asleep states using HRV analysis. Biocybernetics and Biomedical Engineering, 37(2), pp.290-301.
      [29] Zhang, C. and Schm?cker, J.D., 2017. A Markovian model of user adaptation with case study of a shared bicycle scheme. Transportmetrica B: Transport Dynamics, pp.1-14.
      [30] Zhang, L., Wade, J., Bian, D., Fan, J., Swanson, A., Weitlauf, A., Warren, Z. and Sarkar, N., 2017. Cognitive load measurement in a virtual reality-based driving system for autism intervention. IEEE transactions on affective computing, 8(2), pp.176-189.
      [31] Sauvet, F., Bougard, C., Coroenne, M., Lely, L., Van Beers, P., Elbaz, M., Guillard, M., Leger, D. and Chennaoui, M., 2014. In-flight automatic detection of vigilance states using a single EEG channel. IEEE Transactions on Biomedical Engineering, 61(12), pp.2840-2847.
      [32] Graditi, G., Ferlito, S., Adinolfi, G., Tina, G.M. and Ventura, C., 2016. Energy yield estimation of thin-film photovoltaic plants by using physical approach and artificial neural networks. Solar Energy, 130, pp.232-243.
      [33] Rosipal, R., Peters, B., Kecklund, G., ?kerstedt, T., Gruber, G., Woertz, M., Anderer, P. and Dorffner, G., 2007, July. EEG-based drivers' drowsiness monitoring using a hierarchical Gaussian mixture model. In International Conference on Foundations of Augmented Cognition (pp. 294-303). Springer, Berlin, Heidelberg.
      [34] Mandal, N.K. and Pal, M., 2008. Optimum mixture design using deficiency criterion. Communications in Statistics-Theory and Methods, 37(10), pp.1565-1575.
      [35] Bulling, A., Ward, J.A., Gellersen, H. and Troster, G., 2011. Eye movement analysis for activity recognition using electrooculography. IEEE transactions on pattern analysis and machine intelligence, 33(4), pp.741-753.
      [36] Chang, W.D., Cha, H.S. and Im, C.H., 2016. Removing the Interdependency between Horizontal and Vertical Eye-Movement Components in Electrooculograms. Sensors, 16(2), p.227.
      [37] Chen, L.L., Zhao, Y., Zhang, J. and Zou, J.Z., 2015. Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Systems with Applications, 42(21), pp.7344-7355.
      [38] Han, W., Li, X. and Xi, J., 2018. A statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation. IET Intelligent Transport Systems.
      [39] Flad, N., Fomina, T., Buelthoff, H.H. and Chuang, L.L., 2015, October. Unsupervised clustering of EOG as a viable substitute for optical eye tracking. In Workshop on Eye Tracking and Visualization (pp. 151-167). Springer, Cham.
      [40] Dawson, D., Searle, A.K. and Paterson, J.L., 2014. Look before you (s) leep: evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry. Sleep medicine reviews, 18(2), pp.141-152.
      [41] Fuhl, W., Santini, T.C., Kbler, T. and Kasneci, E., 2016, March. Else: Ellipse selection for robust pupil detection in real-world environments. In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (pp. 123-130). ACM.
      [42] Gao, X.Y., Zhang, Y.F., Zheng, W.L. and Lu, B.L., 2015, April. Evaluating driving fatigue detection algorithms using eye tracking glasses. In Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on (pp. 767-770). IEEE.
      [43] Othman, N. and Dorizzi, B., 2015. Impact of quality-based fusion techniques for video-based iris recognition at a distance. IEEE transactions on Information Forensics and Security, 10(8), pp.1590-1602.
      [44] Gou, C., Wu, Y., Wang, K., Wang, K., Wang, F.Y. and Ji, Q., 2017. A joint cascaded framework for simultaneous eye detection and eye state estimation. Pattern Recognition, 67, pp.23-31.
      [45] Liang, Y., Horrey, W.J., Howard, M.E., Lee, M.L., Anderson, C., Shreeve, M.S., O'Brien, C.S. and Czeisler, C.A., 2017. Prediction of drowsiness events in night shift workers during morning driving. Accident Analysis & Prevention.
      [46] McDonnell, D., Galvanic Ltd, 2017. Electrodermal Activity Sensor. U.S. Patent Application 15/124,372.
      [47] McIntire, L.K., McKinley, R.A., Goodyear, C. and McIntire, J.P., 2017, May. Use of head-worn sensors to detect lapses in vigilance through the measurement of PERCLOS and cerebral blood flow velocity. In Disruptive Technologies in Sensors and Sensor Systems (Vol. 10206, p. 102060K). International Society for Optics and Photonics.
      [48] Meng, S.H., Hu, S.B., Huang, A.C., Huang, T.J., Xie, Z. and Jian, C., 2017, October. Research on Eye Detection and Fatigue Early Warning Technologies. In The Euro-China Conference on Intelligent Data Analysis and Applications (pp. 3-9). Springer, Cham.
      [49] M. Nagesh, Md. Zia Ur Rahman, "A New ECG Signal Enhancement Strategy using Non-Negative Algorithms", International Journal of Control Theory and Applications Vol.10, no.35, 2017, pp.323-333.
      [50] G V S Karthik, Md. Zia Ur Rahman, "ECG Signal Enhancement using Circular Leaky Adaptive Algorithm in an IOT Enabled Sensor System", International Journal of Control Theory and Applications,Vol.10, no.35, 2017, pp.271-282.

      [51] Md. Salman, Md. Zia Ur Rahman, "Efficient and Low Complexity Noise Cancellers for Cardiac Signal Enhancement using Proportionate Adaptive Algorithms", Indian Journal Science and Technology,Vol.9, no-37, pp. 1-11, October 2016.
      [52] M. Nagesh, Md. Zia Ur Rahman, "Efficient Noise Cancellers for ECG Signal Enhancement for Telecardiology Applications", Leonardo Electronic Journal of Practices and Technologies,Issue 29, 2016, pp.79-92.
      [53] M. Nagesh, Md. Zia Ur Rahman, "Efficient Cardiac Signal Enhancement Techniques Based on Variable Step Size and Data Normalized Hybrid Signed Adaptive Algorithms", International Review on Computers and Software,Vol.11, no.10, 2016, pp.872-883.
      [54] B. Srikanth, Md. Zia Ur Rahman, "Efficient ECG Signal Conditioning Techniques using Variable Step Size LMF Algorithms", International Journal of Engineering and Technology,Vol. 8, No 2, pp.660-668, 2016.
      [55] Asiya Sulthana, Md. Zia Ur Rahman, "Design and Implementation of Efficient Low Complexity Biomedical Artifact Canceller for Nano Devices", Leonardo Electronic Journal of Practices and Technologies,Issue 28, pp. 197-210, 2016.
      [56] Md. Zia Ur Rahman, et. al., "Artifact Removal in ECG Signals using Modified Data Normalization Based Signal Enhancement Units for Health Care Monitoring Systems", Journal of Theoretical and Applied Information Technology,Vol.93, no.2, 2016, pp.540-551.
      [57] Sk. Nore Johny Basha, Md Zia-Ur-Rahman, and Dr.B.V. Rama Mohana Rao, " Noise Removal from Electrocardiogram Signals using Leaky and Normalized version of Adaptive Noise Canceller," International Journal of Computer Science & Communication Networks, ISSN: 2249-5789, Vol. 1(1), Sep.-Oct. 2011.
      [58] Md. Zia Ur Rahman, S.R.Ahamed, D.V.R.K Reddy, "Efficient and Simplified Adaptive Noise Cancellers for ECG Sensor based Remote Health Monitoring", IEEE Sensors Journal, vol.12, no.3, pp. 566-573, 2012.
      [59] Md. Zia Ur Rahman, S.R.Ahamed, D.V.R.K Reddy, "Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry", Elsevier Signal Processing, vol.91, pp. 225-239, 2011.
      [60] Sowmya I, Ch. Sathi Raju, Md Zia-Ur-Rahman, D.V.R.K Reddy "Respiration Baseline wander removal from cardiac signals using an optimized Adaptive Noise canceller", Canadian Journal of Signal Processing, Vol-2, no-3, pp.27-31, 2011.
      [61] Md.Zia Ur Rahman, S.R.Ahamed and D.V.R.K Reddy, "Noise Cancellation in ECG Signals using Computationally Simplified Adaptive Filtering Techniques: Application to Biotelemetry", Signal Processing: An International Journal, CSC Journals, ISSN 1985-2312, Vol. 3, Issue 5, pp. 1-12, 2009.
      [62] Md. Nizamuddin Salman, P. Trinatha Rao, Md.Zia Ur Rahman, "Cardiac Signal Enhancement Using Normalised Variable Step Algorithm For Remote Healthcare Monitoring Systems," International Journal of Medical Engineering and Informatics, Inderscience Pub, Vol. 9, No. 2, 2017, pp. 145-161.
      [63] Md. Zia Ur Rahman, Adaptive Noise Cancellers for Cardiac Signal Enhancement for IOT Based Health Care Systems, Journal of Theoretical and Applied Information Technology,Vol.95, no.10, 2017, pp.2206-2213.

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    K Firke, O., & Manish Jain, D. (2018). Analysis of Driver Drowsiness Detection using EEG and EOG. International Journal of Engineering & Technology, 7(2.17), 46-51. https://doi.org/10.14419/ijet.v7i2.17.11557