A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 8 Issue 7
Jul.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(7): 1222-1242
C. Zhang, A. Eskandarian, "A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1222-1242, Jul. 2021. doi: 10.1109/JAS.2020.1003450
Citation: C. Zhang, A. Eskandarian, "A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1222-1242, Jul. 2021. doi: 10.1109/JAS.2020.1003450
shu

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

doi: 10.1109/JAS.2020.1003450

  • Virginia Tech Mechanical Engineering Department ASIM Lab, Virginia 24061-0131 USA

More Information
    Abstract
  • The driver’s cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.

     

    • FullText(HTML)
  • loading
    • References(136)
  • [1]
    NHTSA, “2016 fatal motor vehicle crashes: Overview,” National Highway Traffic Safety Administration, Washington DC, USA, Report No. DOT HS 812 456, 2017.
    [2]
    A. Broggi, P. Cerri, S. Debattisti, M. C. Laghi, P. Medici, M. Panciroli, and A. Prioletti, “PROUD-Public road urban driverless test: Architecture and results,” in Proc. IEEE Intelligent Vehicles Symp. Proc., Dearborn, USA, 2014, pp. 648–654.
    [3]
    V. D. Pyrialakou, C. Gkartzonikas, J. D. Gatlin, and K. Gkritza, “Perceptions of safety on a shared road: Driving, cycling, or walking near an autonomous vehicle,” J. Safety Res., vol. 72, pp. 249–258, Feb. 2020. doi: 10.1016/j.jsr.2019.12.017
    [4]
    M. Q. Khan and S. Lee, “A comprehensive survey of driving monitoring and assistance systems,” Sensors, vol. 19, no. 11, Article No. 2574, Jun. 2019. doi: 10.3390/s19112574
    [5]
    Lexus, LS 600h L Owner’s Manual, pp. 234–236, 2008. [Online]. Available: https://drivers.lexus.com/t3Portal/document/om-s/OM50894U/pdf/OM50894U.pdf
    [6]
    BMW, Owner’s Manual The BMW X5, 237, 2019. [Online]. Available: https://ownersmanuals2.com/bmw-auto/x5-2019-owners-manual-76615
    [7]
    Tesla, Model 3 Owner’s Manual, 110–112, 2020. [Online]. Available: https://www.tesla.com/sites/default/files/model_3_owners_manual_north_america_en.pdf
    [8]
    H. M. Lu, Q. Liu, D. X. Tian, Y. J. Li, H. Kim, and S. Serikawa, “The cognitive internet of vehicles for autonomous driving,” IEEE Netw., vol. 33, no. 3, pp. 65–73, May–Jun. 2019. doi: 10.1109/MNET.2019.1800339
    [9]
    Y. J. Li, Y. Jiang, D. X. Tian, L. Hu, H. M. Lu, and Z. Y. Yuan, “AI-enabled emotion communication,” IEEE Netw., vol. 33, no. 6, pp. 15–21, Nov.–Dec. 2019. doi: 10.1109/MNET.001.1900070
    [10]
    H. M. Lu, M. Wang, and A. K. Sangaiah, “Human emotion recognition using an EEG cloud computing platform,” Mobile Netw. Appl., vol. 25, no. 3, pp. 1023–1032, Jun. 2020. doi: 10.1007/s11036-018-1120-1
    [11]
    S. N. Abdulkader, A. Atia, and M. S. M. Mostafa, “Brain computer interfacing: Applications and challenges,” Egyp. Inform. J., vol. 16, no. 2, pp. 213–230, Jul. 2015. doi: 10.1016/j.eij.2015.06.002
    [12]
    S. F. Liang, C. T. Lin, R. C. Wu, Y. C. Chen, T. Y. Huang, and T. P. Jung, “Monitoring driver’s alertness based on the driving performance estimation and the EEG power spectrum analysis,” in Proc. IEEE Eng. in Medicine and Biology 27th Annu. Conf., Shanghai, China, 2006, pp. 5738–5741.
    [13]
    F. W. Wang, Q. Xu, and R. R. Fu, “Study on the effect of man-machine response mode to relieve driving fatigue based on EEG and EOG,” Sensors, vol. 19, no. 22, Article No. 4883, Nov. 2019. doi: 10.3390/s19224883
    [14]
    M. Lemke, “Correlation between eeg and driver’s actions during prolonged driving under monotonous conditions,” Accid. Anal. Prev., vol. 14, no. 1, pp. 7–17, Feb. 1982. doi: 10.1016/0001-4575(82)90003-3
    [15]
    K. Idogawa, S. P. Ninomija, and F. Yano, “A time variation of professional driver’s EEG in monotonous work,” in Proc. Annu. Int. Eng. in Medicine and Biology Society, Images of the 21st Century, Seattle, USA, 1989, pp. 719–720.
    [16]
    S. K. L. Lal, A. Craig, P. Boord, L. Kirkup, and H. Nguyen, “Development of an algorithm for an EEG-based driver fatigue countermeasure,” J. Safety Res., vol. 34, no. 3, pp. 321–328, Aug. 2003. doi: 10.1016/S0022-4375(03)00027-6
    [17]
    D. Göhring, D. Latotzky, M. Wang, and R. Rojas, “Semi-autonomous car control using brain computer interfaces,” in Intelligent Autonomous Systems 12, S. Lee, H. Cho, K. J. Yoon, and J. Lee, Eds. Berlin, Heidelberg: Springer, 2013, pp. 393–408.
    [18]
    A. Eskandarian, “Safety issues of drowsy/fatigue driving and countermeasure mitigation,” in Proc. Road Safety on Four Continents: 15th Int. Conf., Abu Dhabi, United Arab, 2010.
    [19]
    R. A. Sayed, A. Eskandarian, and P. Delaigue, “Driver fatigue: Causes and countermeasures,” in Proc. Int. Truck and Bus Safety and Security Symp., Alexandria, USA, 2005.
    [20]
    J. D. Lee, J. Moeckli, T. L. Brown, S. C. Roberts, C. Schwarz, L. Yekhshatyan, E. Nadler, Y. L. Liang, T. Victor, D. Marshall, and C. Davis, “Distraction detection and mitigation through driver feedback,” National Highway Traffc Safety Administration, Washington DC, United States, Report No. DOT HS 811 547A, 2013.
    [21]
    A. Eskandarian, R. A. Sayed, P. Delaigue, A. Mortazavi, and J. J. Blum, “Advanced driver fatigue research,” U.S. Department of Transportation, 2007. [Online]. Available: https://ntlrepository.blob.core.windows.net/lib/51000/51200/51269/Advanced-Driver-Fatigue-Research-Final-Report-April2007.pdf
    [22]
    A. Eskandarian and A. Mortazavi, “Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection,” in Proc. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, 2007, pp. 553–559.
    [23]
    A. Eskandarian, Handbook of Intelligent Vehicles. London, Britain: Springer-Verlag London, 2012, pp. 1599.
    [24]
    A. Eskandarian and A. Mortazavi, “Unobtrusive driver drowsiness detection system and method,” U.S. Patent 8519853, Nov. 9, 2009.
    [25]
    M. Z. Guo, S. W. Li, L. H. Wang, M. Chai, F. C. Chen, and Y. N. Wei, “Research on the relationship between reaction ability and mental state for online assessment of driving fatigue,” Int. J. Environ. Res. Public Health, vol. 13, no. 12, Article No. 1174, Nov. 2016. doi: 10.3390/ijerph13121174
    [26]
    D. McCarthy, “Taxonomy of older driver behaviors and crash risk,” National Highway Traffic Safety Administration, 2018.
    [27]
    J. G. Gaspar and D. V. McGehee, “Driver brake response to sudden unintended acceleration while parking,” Trans. Res. Interd. Perspect., vol. 2, Article No. 100039, Sep. 2019.
    [28]
    Y. Xing, C. Lv, and D. P. Cao, “Chapter 5 – Driver behavior recognition in driver intention inference systems,” in Advanced Driver Intention Inference, Y. Xing, C. Lv, and D. P. Cao, Eds. Oxford, UK: Elsevier, 2020, pp. 99–134.
    [29]
    J. W. Kim, H. I. Suk, J. P. Kim, and S. W. Lee, “Combined regression and classification approach for prediction of driver’s braking intention,” in Proc. 3rd Int. Winter Conf. Brain-Computer Interface, Sabuk, South Korea, 2015, pp. 1–3.
    [30]
    I. H. Kim, J. W. Kim, S. Haufe, and S. W. Lee, “Detection of braking intention in diverse situations during simulated driving based on EEG feature combination,” J. Neural Eng., vol. 12, no. 1, Article No. 016001, Feb. 2015. doi: 10.1088/1741-2560/12/1/016001
    [31]
    T. Teng, L. Z. Bi, and Y. L. Liu, “EEG-based detection of driver emergency braking intention for brain-controlled vehicles,” IEEE Trans. Intell. Trans. Syst., vol. 19, no. 6, pp. 1766–1773, Jun. 2018. doi: 10.1109/TITS.2017.2740427
    [32]
    S. Haufe, J. W. Kim, A. Sonnleitner, M. Schrauf, G. Curio, and B. Blankertz, “Electrophysiology-based detection of emergency braking intention in real-world driving,” J. Neural Eng., vol. 11, no. 5, Article No. 056011, Oct. 2014. doi: 10.1088/1741-2560/11/5/056011
    [33]
    K. E. Mathewson, T. J. L. Harrison, and S. A. D. Kizuk, “High and dry? Comparing active dry EEG electrodes to active and passive wet electrodes” Psychophysiology, vol. 54, no. 1, pp. 74–82, Jan. 2017. doi: 10.1111/psyp.12536
    [34]
    E. R. Symeonidou, A. D. Nordin, W. D. Hairston, and D. P. Ferris, “Effects of cable sway, electrode surface area, and electrode mass on electroencephalography signal quality during motion,” Sensors, vol. 18, no. 4, Article No. 1073, Apr. 2018. doi: 10.3390/s18041073
    [35]
    Á. Török, I. Sulykos, K. Kecskés-Kovács, G. Persa, P. Galambos, A. Kóbor, I. Czigler, V. Csépe, P. Baranyi, and F. Honbolygó, “Comparison between wireless and wired EEG recordings in a virtual reality lab: Case report,” in Proc. 5th IEEE Conf. Cognitive Infocommunications, Vietri sul Mare, Italy, 2014, pp. 599–603.
    [36]
    A. Eskandarian, C. X. Wu, and C. Y. Sun, “Research advances and challenges of autonomous and connected ground vehicles,” IEEE Trans. Intell. Trans. Syst., pp. 1–29, Dec. 2019. DOI: 10.1109/TITS.2019.2958352.
    [37]
    D. Barwick, “Clinical electroencephalography and topographic brain mapping technology and practice,” J. Neurol. Neuros. Psych., vol. 52, no. 11, pp. 1322–1323, Nov. 1989.
    [38]
    M. A. Regan, J. D. Lee, and T. W. Victor, Driver Distraction and Inattention: Advances in Research and Countermeasures. London, Britain: CRC Press, 2013, pp. 464.
    [39]
    T. H. Nguyen and W. Y. Chung, “Detection of driver braking intention using EEG signals during simulated driving,” Sensors, vol. 19, no. 13, Article No. 2863, Jun. 2019. doi: 10.3390/s19132863
    [40]
    J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng., vol. 16, no. 1, pp. 407–413, Feb. 2014.
    [41]
    R. Foong, K. K. Ang, Z. Zhang, and C. Quek, “An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue,” J. Neural Eng., vol. 16, no. 5, Article No. 056013, Aug. 2019. doi: 10.1088/1741-2552/ab255d
    [42]
    R. Foong, K. K. Ang, and C. Quek, “Correlation of reaction time and EEG log bandpower from dry frontal electrodes in a passive fatigue driving simulation experiment,” in Proc. 39th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Seogwipo, South Korea, 2017, pp. 2482–2485.
    [43]
    M. Sazgar and M. G. Young, “EEG artifacts,” in Absolute Epilepsy and EEG Rotation Review: Essentials for Trainees, M. Sazgar and M. G. Young, Eds. Cham: Springer, 2019, pp. 149–162.
    [44]
    X. Jiang, G. B. Bian, and Z. A. Tian, “Removal of artifacts from EEG signals: A review,” Sensors, vol. 19, no. 5, Article No. 987, Feb. 2019. doi: 10.3390/s19050987
    [45]
    R. N. Vigário, “Extraction of ocular artefacts from EEG using independent component analysis,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 395–404, 1997. doi: 10.1016/S0013-4694(97)00042-8
    [46]
    S. Y. Hu, G. T. Zheng, and B. Peters, “Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,” IET Intell. Trans. Syst., vol. 7, no. 1, pp. 105–113, Mar. 2013. doi: 10.1049/iet-its.2012.0045
    [47]
    M. T. Akhtar, W. Mitsuhashi, and C. J. James, “Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data,” Signal Process., vol. 92, no. 2, pp. 401–416, Feb. 2012. doi: 10.1016/j.sigpro.2011.08.005
    [48]
    S. Barua, M. U. Ahmed, C. Ahlstrom, S. Begum, and P. Funk, “Automated EEG artifact handling with application in driver monitoring,” IEEE J. Biomed. Health Inf., vol. 22, no. 5, pp. 1350–1361, Sept. 2018. doi: 10.1109/JBHI.2017.2773999
    [49]
    I. Winkler, S. Haufe, and M. Tangermann, “Automatic classification of artifactual ICA-components for artifact removal in EEG signals,” Behav Brain Funct., vol. 7, no. 1, Article No. 30, Aug. 2011. doi: 10.1186/1744-9081-7-30
    [50]
    W. De Clercq, A. Vergult, B. Vanrumste, W. Van Paesschen, and S. Van Huffel, “Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram,” IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2583–2587, Nov. 2006. doi: 10.1109/TBME.2006.879459
    [51]
    A. S. Janani, T. S. Grummett, T. W. Lewis, S. P. Fitzgibbon, E. M. Whitham, D. DelosAngeles, H. Bakhshayesh, J. O. Willoughby, and K. J. Pope, “Improved artefact removal from EEG using canonical correlation analysis and spectral slope,” J. Neurosci. Methods, vol. 298, pp. 1–15, Mar. 2018. doi: 10.1016/j.jneumeth.2018.01.004
    [52]
    M. H. Soomro, N. Badruddin, M. Z. Yusoff, and M. A. Jatoi, “Automatic eye-blink artifact removal method based on EMD-CCA,” in Proc. ICME Int. Conf. Complex Medical Engineering, Beijing, China, 2013, pp. 186–190.
    [53]
    J. F. Gao, C. X. Zheng, and P. Wang, “Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis,” Clin. EEG Neurosci., vol. 41, no. 1, pp. 53–59, Jan. 2010. doi: 10.1177/155005941004100111
    [54]
    C. T. Lin, C. S. Huang, W. Y. Yang, A. K. Singh, C. H. Chuang, and Y. K. Wang, “Real-time EEG signal enhancement using canonical correlation analysis and gaussian mixture clustering,” J. Healthc. Eng., vol. 2018, Article No. 5081258, Jan. 2018.
    [55]
    MathWorks, MATLAB: The Language of Technical Computing: Computation, Visualization, Programming: Version 5. Natwick, USA: MathWorks Inc., 1996.
    [56]
    L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. Vanderplas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: Experiences from the scikit-learn project,” arXiv preprint arXiv: 1309.0238, 2013.
    [57]
    R. Polikar. The wavelet tutorial: The Engineer’s ultimate guide to wavelet analysis. [Online]. Available: http://users.rowan.edu/~polikar/WTtutorial.html.
    [58]
    S. Khatun, R. Mahajan, and B. I. Morshed, “Comparative study of wavelet-based unsupervised ocular artifact removal techniques for single-channel EEG data,” IEEE J. Trans. Eng. Health Med., vol. 4, Article No. 2000108, Mar. 2016.
    [59]
    V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, and R. Kalidoss, “Automatic identification and removal of ocular artifacts from EEG using wavelet transform,” Meas. Sci. Rev., vol. 6, no. 4, pp. 45–57, 2006.
    [60]
    J. C. Woestenburg, M. N. Verbaten, and J. L. Slangen, “The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain,” Biol. Psychol., vol. 16, no. 1–2, pp. 127–147, Feb.–Mar. 1983. doi: 10.1016/0301-0511(83)90059-5
    [61]
    J. L. Kenemans, P. C. M. Molenaar, M. N. Verbaten, and J. L. Slangen, “Removal of the ocular artifact from the EEG: A comparison of time and frequency domain methods with simulated and real data,” Psychophysiology, vol. 28, no. 1, pp. 114–121, Jan. 1991. doi: 10.1111/j.1469-8986.1991.tb03397.x
    [62]
    M. M. N. Mannan, M. A. Kamran, S. Kang, and M. Y. Jeong, “Effect of EOG signal filtering on the removal of ocular artifacts and EEG-Based brain-computer interface: A comprehensive study,” Complexity, vol. 2018, Article No. 4853741, Jul. 2018.
    [63]
    M. F. Issa and Z. Juhasz, “Improved EOG artifact removal using wavelet enhanced independent component analysis,” Brain Sci., vol. 9, no. 12, Article No. 355, Dec. 2019. doi: 10.3390/brainsci9120355
    [64]
    M. M. N. Mannan, M. Y. Jeong, and M. A. Kamran, “Hybrid ICA-regression: Automatic identification and removal of ocular artifacts from electroencephalographic signals,” Front. Human Neurosc., vol. 10, Article No. 193, Apr. 2016.
    [65]
    Z. M. Hira and D. F. Gillies, “A review of feature selection and feature extraction methods applied on microarray data,” Adv. Bioinformatics, vol. 2015, Article No. 198363, Jun. 2015.
    [66]
    S. Sur and V. K. Sinha, “Event-related potential: An overview,” Ind. Psychiatry J., vol. 18, no. 1, pp. 70–73, Jan. 2009. doi: 10.4103/0972-6748.57865
    [67]
    G. Pfurtscheller, “Functional brain imaging based on ERD/ERS,” Vision Res., vol. 41, no. 10–11, pp. 1257–1260, May 2001. doi: 10.1016/S0042-6989(00)00235-2
    [68]
    Giovanni, T. Suprihadi, and K. Karyono, “DROWTION: Driver drowsiness detection software using MINDWAVE,” in Proc. Int. Conf. Industrial Automation, Information and Communications Technology, Bali, Indonesia, 2014, pp. 141–144.
    [69]
    F. E. Bloom, “Chapter 1 – Fundamentals of neuroscience,” in Fundamental Neuroscience, 4th ed. L. R. Squire, D. Berg, F. E. Bloom, S. du Lac, A. Ghosh, and N. C. Spitzer, Eds. San Diego, USA: Academic Press, 2013, pp. 3–13.
    [70]
    M. S. Gazzaniga and G. R. Mangun, The Cognitive Neurosciences, 5th ed. Cambridge, MA, US: MIT Press, 2014, pp. xvi, 1106-xvi, 1106.
    [71]
    Y. J. Wang, S. K. Gao, and X. R. Gao, “Common spatial pattern method for channel selelction in motor imagery based brain-computer interface,” in Proc. IEEE Engineering in Medicine and Biology 27th Annu. Conf., Shanghai, China, 2005, pp. 5392–5395.
    [72]
    J. W. Kim, I. H. Kim, S. Haufe, and S. W. Lee, “Brain-computer interface for smart vehicle: Detection of braking intention during simulated driving,” in Proc. Int. Winter Workshop on Brain-Computer Interface, Jeongsun-kun, South Korea, 2014, pp. 1–3.
    [73]
    C. Dijksterhuis, D. de Waard, K. A. Brookhuis, B. L. J. M. Mulder, and R. de Jong, “Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns,” Front. Neurosci., vol. 7, Article No. 149, Aug. 2013.
    [74]
    G. Dornhege, B. Blankertz, G. Curio, and K. R. Müller, “Increase information transfer rates in BCI by CSP extension to multi-class,” in Proc. 17th Int. Conf. Neural Information Processing Systems, Whistler, British Columbia, Canada, 2003.
    [75]
    W. Wu, X. R. Gao, and S. K. Gao, “One-versus-the-rest (OVR) algorithm: An extension of common spatial patterns (CSP) algorithm to multi-class case,” in Proc. IEEE Engineering in Medicine and Biology 27th Annu. Conf., Shanghai, China, 2005, pp. 2387–2390.
    [76]
    M. Grosse-Wentrup and M. Buss, “Multiclass common spatial patterns and information theoretic feature extraction,” IEEE Trans. Biomed. Eng., vol. 55, no. 8, pp. 1991–2000, Aug. 2008. doi: 10.1109/TBME.2008.921154
    [77]
    K. K. Ang, Z. Y. Chin, H. H. Zhang, and C. T. Guan, “Filter bank common spatial pattern (FBCSP) in brain-computer interface,” in Proc. IEEE Int. Joint Conf. Neural Networks (IEEE World Congr. Computational Intelligence), Hong Kong, China, 2008, pp. 2390–2397.
    [78]
    X. M. Song and S. C. Yoon, “Improving brain–computer interface classification using adaptive common spatial patterns,” Comput. Biol. Med., vol. 61, pp. 150–160, Jun. 2015. doi: 10.1016/j.compbiomed.2015.03.023
    [79]
    A. P. Costa, J. S. Møller, H. K. Iversen, and S. Puthusserypady, “An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm,” Comput. Biol. Med., vol. 103, pp. 24–33, Dec. 2018. doi: 10.1016/j.compbiomed.2018.09.021
    [80]
    H. B. Yu, H. T. Lu, S. H. Wang, K. J. Xia, Y. Z. Jiang, and P. J. Qian, “A general common spatial patterns for EEG analysis with applications to vigilance detection,” IEEE Access, vol. 7, pp. 111102–111114, Aug. 2019. doi: 10.1109/ACCESS.2019.2934519
    [81]
    M. X. Cohen, “The discrete time Fourier transform, the FFT, and the convolution theorem,” in Analyzing Neural Time Series Data: Theory and Practice. Cambridge, MA, USA: MIT Press, 2014.
    [82]
    Y. K. Wang, T. P. Jung, and C. T. Lin, “EEG-based attention tracking during distracted driving,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 6, pp. 1085–1094, Nov. 2015. doi: 10.1109/TNSRE.2015.2415520
    [83]
    T. Oliphant, “Basic modules,” in Guide to NumPy. Continuum Press, 2015.
    [84]
    A. Y. Kaplan, A. A. Fingelkurts, A. A. Fingelkurts, S. V. Borisov, and B. S. Darkhovsky, “Nonstationary nature of the brain activity as revealed by EEG/MEG: Methodological, practical and conceptual challenges,” Signal Process., vol. 85, no. 11, pp. 2190–2212, Nov. 2005. doi: 10.1016/j.sigpro.2005.07.010
    [85]
    H. S. AlZu’bi, W. Al-Nuaimy, and N. S. Al-Zubi, “EEG-based driver fatigue detection,” in Proc. 6th Int. Conf. Developments in eSystems Engineering, Abu Dhabi, United Arab Emirates, 2013, pp. 111–114.
    [86]
    F. Mohamed, S. F. Ahmed, Z. Ibrahim, and S. Yaacob, “Comparison of features based on spectral estimation for the analysis of EEG signals in driver behavior,” in Proc. Int. Conf. Computational Approach in Smart Systems Design and Applications, Kuching, Malaysia, 2018, pp. 1–7.
    [87]
    C. T. Lin, S. A. Chen, T. T. Chiu, H. Z. Lin, and L. W. Ko, “Spatial and temporal EEG dynamics of dual-task driving performance,” J. Neuroeng. Rehabil., vol. 8, Article No. 11, Feb. 2011. doi: 10.1186/1743-0003-8-11
    [88]
    M. Awais, N. Badruddin, and M. Drieberg, “EEG brain connectivity analysis to detect driver drowsiness using coherence,” in Proc. Int. Conf. Frontiers of Information Technology, Islamabad, Pakistan, 2017, pp. 110–114.
    [89]
    S. J. Jung, H. S. Shin, and W. Y. Chung, “Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel,” IET Intell. Trans. Syst., vol. 8, no. 1, pp. 43–50, Feb. 2014. doi: 10.1049/iet-its.2012.0032
    [90]
    C. Dumitrescu, I. M. Costea, F. Nemtanu, I. Badescu, and A. Banica, “Developing a multi sensors system to detect sleepiness to drivers from transport systems,” in Proc. IEEE 22nd Int. Symp. Design and Technology in Electronic Packaging, Oradea, Romania, 2016, pp. 175–178.
    [91]
    M. Apoorva, “Power spectrum density estimation methods for michelson interferometer wavememters,” Master of Applied Science, Electrical and Computer Engineering, University of Ottawa, Ottawa, ON, Canada, 2016.
    [92]
    J. Cecconi and J. Cecconi, “Spectral analysis” (in English.), 2011. [Online]. Available: 2010.
    [93]
    R. Chai, G. R. Naik, T. N. Nguyen, S. H. Ling, Y. Tran, A. Craig, and H. T. Nguyen, “Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system,” IEEE J. Biomed. Health Inf., vol. 21, no. 3, pp. 715–724, May 2017. doi: 10.1109/JBHI.2016.2532354
    [94]
    Z. Z. Guo, Y. F. Pan, G. Z. Zhao, S. Cao, and J. Zhang, “Detection of driver vigilance level using EEG signals and driving contexts,” IEEE Trans. Reliabil., vol. 67, no. 1, pp. 370–380, Mar. 2018. doi: 10.1109/TR.2017.2778754
    [95]
    A. Picot, S. Charbonnier, and A. Caplier, “On-line detection of drowsiness using brain and visual information,” IEEE Trans. Syst.,Man,Cybernet. – Part A:Syst. Humans, vol. 42, no. 3, pp. 764–775, May 2012.
    [96]
    X. Q. Huo, W. L. Zheng, and B. L. Lu, “Driving fatigue detection with fusion of EEG and forehead EOG,” in Proc. Int. Joint Conf. Neural Networks, Vancouver, Canada, 2016, pp. 897–904.
    [97]
    V. Alizadeh and O. Dehzangi, “The impact of secondary tasks on drivers during naturalistic driving: Analysis of EEG dynamics,” in Proc. IEEE 19th Int. Conf. Intelligent Transportation Systems, Rio de Janeiro, Brazil, 2016, pp. 2493–2499.
    [98]
    A. G. Correa and E. L. Leber, “An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records,” in Proc. Annu. Int. Conf. IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, 2010, pp. 1405–1408.
    [99]
    B. P. Nayak, S. Kar, A. Routray, and A. K. Padhi, “A biomedical approach to retrieve information on driver’s fatigue by integrating EEG, ECG and blood biomarkers during simulated driving session,” in Proc. 4th Int. Conf. Intelligent Human Computer Interaction, Kharagpur, India, 2012, pp. 1–6.
    [100]
    A. Sengupta, A. Routray, and S. Datta, “Brain networks using nonlinear interdependence-based EEG synchronization: A study of human fatigue,” in Proc. Int. Conf. Systems in Medicine and Biology, Kharagpur, India, 2016, pp. 170–173.
    [101]
    M. Mohammadpour and S. Mozaffari, “Classification of EEG-based attention for brain computer interface,” in Proc. 3rd Iranian Conf. Intelligent Systems and Signal Processing, Shahrood, Iran, 2017, pp. 34–37.
    [102]
    R. N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm,” IEEE Trans. Biomed. Eng., vol. 58, no. 1, pp. 121–131, Jan. 2011. doi: 10.1109/TBME.2010.2077291
    [103]
    C. Zhang, F. Y. Cong, and H. Wang, “Driver fatigue analysis based on binary brain networks,” in Proc. 7th Int. Conf. Information Science and Technology, Da Nang, Vietnam, 2017, pp. 485–489.
    [104]
    A. Saha, A. Konar, and A. K. Nagar, “EEG analysis for cognitive failure detection in driving using type-2 fuzzy classifiers,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 1, no. 6, pp. 437–453, Dec. 2017. doi: 10.1109/TETCI.2017.2750761
    [105]
    C. T. Lin, L. W. Ko, K. L. Lin, S. F. Liang, B. C. Kuo, I. F. Chung, and L. D. Van, “Classification of driver’s cognitive responses using nonparametric single-trial EEG analysis,” in Proc. IEEE Int. Symp. Circuits and Systems, New Orleans, USA, 2007, pp. 2019–2023.
    [106]
    C. T. Lin, K. L. Lin, L. W. Ko, S. F. Liang, B. C. Kuo, and I. F. Chung, “Nonparametric single-trial EEG feature extraction and classification of driver’s cognitive responses,” EURASIP J. Adv. in Signal Process., vol. 2008, no. 1, Article No. 849040, Mar. 2008. doi: 10.1155/2008/849040
    [107]
    C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, no. 3, pp. 379–423, Jul. 1948. doi: 10.1002/j.1538-7305.1948.tb01338.x
    [108]
    J. M. Yentes, N. Hunt, K. K. Schmid, J. P. Kaipust, D. McGrath, and N. Stergiou, “The appropriate use of approximate entropy and sample entropy with short data sets,” Ann. Biomed. Eng., vol. 41, no. 2, pp. 349–365, Feb. 2013. doi: 10.1007/s10439-012-0668-3
    [109]
    U. Budak, V. Bajaj, Y. Akbulut, O. Atila, and A. Sengur, “An effective hybrid model for EEG-based drowsiness detection,” IEEE Sens. J., vol. 19, no. 17, pp. 7624–7631, Sept. 2019. doi: 10.1109/JSEN.2019.2917850
    [110]
    Z. K. Gao, S. Li, Q. Cai, W. D. Dang, Y. X. Yang, C. X. Mu, and P. Hui, “Relative wavelet entropy complex network for improving EEG-based fatigue driving classification,” IEEE Trans. Instrum. Meas., vol. 68, no. 7, pp. 2491–2497, Jul. 2019. doi: 10.1109/TIM.2018.2865842
    [111]
    J. F. Hu, F. Q. Liu, and P. Wang, “EEG-based multiple entropy analysis for assessing driver fatigue,” in Proc. 5th Int. Conf. Transportation Information and Safety, Liverpool, United Kingdom, 2019, pp. 1290–1294.
    [112]
    A. Chaudhuri and A. Routray, “Driver fatigue detection through chaotic entropy analysis of cortical sources obtained from scalp EEG signals,” IEEE Trans. Intell. Trans. Syst., vol. 21, no. 1, pp. 185–198, Jan. 2020. doi: 10.1109/TITS.2018.2890332
    [113]
    Z. Khaliliardali, R. Chavarriaga, L. A. Gheorghe, and J. del R. Millán, “Detection of anticipatory brain potentials during car driving,” in Proc. Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, San Diego, USA, 2012, pp. 3829–3832.
    [114]
    Z. Y. Chin, X. Zhang, C. Wang, and K. K. Ang, “EEG-based discrimination of different cognitive workload levels from mental arithmetic,” in Proc. 40th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Honolulu, USA, 2018, pp. 1984–1987.
    [115]
    T. Teng, L. Z. Bi, and X. A. Fan, “Using EEG to recognize emergency situations for brain-controlled vehicles,” in Proc. IEEE Intelligent Vehicles Symp., Seoul, South Korea, 2015, pp. 1305–1309.
    [116]
    L. Z. Bi, J. W. Zhang, and J. L. Lian, “EEG-based adaptive driver-vehicle interface using variational autoencoder and PI-TSVM,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 27, no. 10, pp. 2025–2033, Oct. 2019. doi: 10.1109/TNSRE.2019.2940046
    [117]
    C. C. Chang and C. J. Lin, “LIBSVM: A library for support vector machines,” ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, Article No. 27, May 2011.
    [118]
    J. F. Hu, “Automated detection of driver fatigue based on adaboost classifier with EEG signals,” Front. Comput. Neurosci., vol. 11, Article No. 72, Jul. 2017. doi: 10.3389/fncom.2017.00072
    [119]
    L. Y. Hu, M. W. Huang, S. W. Ke, and C. F. Tsai, “The distance function effect on k-nearest neighbor classification for medical datasets,” Springerplus, vol. 5, no. 1, Article No. 1304, Aug. 2016. doi: 10.1186/s40064-016-2941-7
    [120]
    N. A. B. Amirudin, N. Saad, S. S. A. Ali, and S. H. Adil, “Detection and analysis of driver drowsiness,” in Proc. 3rd Int. Conf. Emerging Trends in Engineering, Sciences and Technology, Karachi, Pakistan, 2018, pp. 1–9.
    [121]
    T. L. Fine, Feedforward Neural Network Methodology. New York: Springer, 2005.
    [122]
    R. Sayed, A. Eskandarian, and M. Oskard, “Driver drowsiness detection using artificial neural networks,” in Proc. Transportation Research Board 80th Annu. Meeting, Washington D.C., USA, 2001.
    [123]
    C. Zhang, H. Wang, and R. R. Fu, “Automated detection of driver fatigue based on entropy and complexity measures,” IEEE Trans. Intell. Trans. Syst., vol. 15, no. 1, pp. 168–177, Feb. 2014. doi: 10.1109/TITS.2013.2275192
    [124]
    S. C. Ng, C. C. Cheung, A. K. F. Lui, and S. S. Xu, “Magnified gradient function to improve first-order gradient-based learning algorithms,” in Proc. 9th Int. Symp. Neural Networks, Shenyang, China, 2012, pp. 448–457.
    [125]
    L. M. King, H. T. Nguyen, and S. K. L. Lal, “Early driver fatigue detection from electroencephalography signals using artificial neural networks,” in Proc. Int. Conf. IEEE Engineering in Medicine and Biology Society, New York, USA, 2006, pp. 2187–2190.
    [126]
    M. A. Almogbel, A. H. Dang, and W. Kameyama, “EEG-signals based cognitive workload detection of vehicle driver using deep learning,” in Proc. 20th Int. Conf. Advanced Communication Technology, Chuncheon-si Gangwon-do, Korea, 2018, pp. 256–259.
    [127]
    M. Hajinoroozi, Z. J. Mao, T. P. Jung, C. T. Lin, and Y. F. Huang, “EEG-based prediction of driver’s cognitive performance by deep convolutional neural network,” Signal Process.:Image Commun., vol. 47, pp. 549–555, Sept. 2016. doi: 10.1016/j.image.2016.05.018
    [128]
    Z. K. Gao, X. M. Wang, Y. X. Yang, C. X. Mu, Q. Cai, W. D. Dang, and S. Y. Zuo, “EEG-based spatio–temporal convolutional neural network for driver fatigue evaluation,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 9, pp. 2755–2763, Sept. 2019. doi: 10.1109/TNNLS.2018.2886414
    [129]
    M. Hajinoroozi, J. M. Zhang, and Y. F. Huang, “Driver’s fatigue prediction by deep covariance learning from EEG,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Banff, Canada, 2017, pp. 240–245.
    [130]
    M. Cogswell, F. Ahmed, R. Girshick, L. Zitnick, and D. Batra, “Reducing overfitting in deep networks by decorrelating representations,” arXiv preprint arXiv: 1511.06068, 2015.
    [131]
    D. P. Mandic and J. A. Chambers, “Fundamentals,” in Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, D. P. Mandic and J. A. Chambers, Eds. New York, USA: John Wiley & Sons, Ltd, 2001, pp. 9–29.
    [132]
    M. A. Moinnereau, S. Karimian-Azari, T. Sakuma, H. Boutani, L. Gheorghe, and T. H. Falk, “EEG artifact removal for improved automated lane change detection while driving,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Miyazaki, Japan, 2018, pp. 1076–1080.
    [133]
    S. M. Lee, J. W. Kim, and S. W. Lee, “Detecting driver’s braking intention using recurrent convolutional neural networks based EEG analysis,” in Proc. 4th IAPR Asian Conf. Pattern Recognition, Nanjing, China, 2017, pp. 840–845.
    [134]
    Y. T. Liu, Y. Y. Lin, S. L. Wu, C. H. Chuang, and C. T. Lin, “Brain dynamics in predicting driving fatigue using a recurrent self-evolving fuzzy neural network,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 2, pp. 347–360, Feb. 2016. doi: 10.1109/TNNLS.2015.2496330
    [135]
    A. Saha, A. Konar, R. Burman, and A. K. Nagar, “EEG analysis for cognitive failure detection in driving using neuro-evolutionary synergism,” in Proc. Int. Joint Conf. Neural Networks, Beijing, China, 2014, pp. 2108–2115.
    [136]
    A. Konar, Computational Intellingence: Principles, Techniques and Applications. New Delhi, India: Springer, 2005.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(23)  / Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (2996) PDF downloads(160) Cited by()

    Highlights

    • Over 100 driver state estimation papers, mostly focused on Brain EEG waves, have been reviewed critically.
    • A comprehensive survey and short tutorial of the most popular signal processing, conventional machine learning classification, and deep learning algorithms for driver state estimation are presented.
    • The future algorithmic requirements of EEG artifact reduction, real-time processing, and between-subject classification accuracy of driver state estimation are discussed.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return