A study of the effects of advanced driver assistance systems alerts on driver performance

  • Javier Izquierdo-Reyes
  • Ricardo A. Ramirez-MendozaEmail author
  • Martin R. Bustamante-Bello
Technical Paper


This paper deals with the application of interactive engineering through an electroencephalogram (EEG) to detect the level of distraction or concentration of drivers of automotive vehicles. In particular, for the case of alerts, signals or outputs emitted by an advanced driver assistance systems (ADAS) in the intelligent transportation systems context. To do that and based on the state-of-the-art, an experimental protocol to detect distraction by using EEG signals of driver has been developed. Finally, the goal is to detect if drivers paid attention on the road when different kinds of alerts are emitted by the ADAS. In terms of signal processing, the challenge was the noise level in EEG records due to quality of road that had some bumpers and potholes that add noise in records due to movements of drivers. With the proposed protocol, the efficiency and utility of ADAS can be evaluated by designers to create new adaptable cabins to provide the driver a better driving environment reducing distractions according to the neurological profile. New perspectives and discussion are formulated in this paper, for example, to enhance the interactive design of the automotive vehicle cabins.


Passive BCI Driver distraction Advanced driver assistance system 



This research is done by the help of Tecnologico de Monterrey and Consejo Nacional de Ciencia y Tecnología (CONACYT) Mexico, by the scholarship 593255.


  1. 1.
    Allison, B., Graimann, B., Gräser, A.: Why use a BCI if you are healthy. BrainPlay: Brain–Computer Interfaces Games Work. ACE, Advances in Computer Entertainment, pp. 1–5 (2007)Google Scholar
  2. 2.
    Reuschenbach, A., Wang, M., Ganjineh, T., Göhring, D.: IDriver—human machine interface for autonomous cars. In: Proceedings–2011 8th International Conference on Information Technology: New Generations, ITNG 2011, pp. 435–440 (2010). doi: 10.1109/ITNG.2011.83
  3. 3.
    Liao, L.D., Lin, C.T., McDowell, K., Wickenden, A.E., Gramann, K., Jung, T.P., Ko, L.W., Chang, J.Y.: Biosensor technologies for augmented brain–computer interfaces in the next decades. Proc. IEEE 100(SPL CONTENT), 1553–1566 (2012). doi: 10.1109/JPROC.2012.2184829 CrossRefGoogle Scholar
  4. 4.
    Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces—a review. Sensors 12(2), 1211–1279 (2012). doi: 10.3390/s120201211 CrossRefGoogle Scholar
  5. 5.
    Haufe, S., Treder, M.S., Gugler, M.F., Sagebaum, M., Curio, G., Blankertz, B.: EEG potentials predict upcoming emergency brakings during simulated driving. J. Neural Eng. 8(5), 056,001 (2011). doi: 10.1088/1741-2560/8/5/056001 CrossRefGoogle Scholar
  6. 6.
    Shin, D., Kim, T., Kim, S., Shin, D.: Design and implementation of smart driving system using context recognition system. In: Proceedings of the IEEE Symposium on Computers and Informatics, pp. 84–89 (2011). doi: 10.1109/ISCI.2011.5958889
  7. 7.
    Lin, C.t., Chen, S.a., Ko, L.w., Wang, Y.k., Acquisition, A.E.E.G.S.: EEG-based brain dynamics of driving distraction. In: Proceedings of International Joint Conference on Neural Networks, pp. 1497–1500 (2011)Google Scholar
  8. 8.
    Wei, C.S., Chuang, S.W., Wang, W.R., Ko, L.W., Jung, T.P., Lin, C.T.: Implementation of a motion sickness evaluation system based on EEG spectrum analysis. In: Proceedings—IEEE International Symposium on Circuits and Systems, pp. 1081–1084 (2011). doi: 10.1109/ISCAS.2011.5937757
  9. 9.
    Borghini, G., Vecchiato, G., Toppi, J., Astolfi, L., Maglione, A., Isabella, R., Caltagirone, C., Kong, W., Wei, D., Zhou, Z., Polidori, L., Vitiello, S., Babiloni, F.: Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices. In: Conference Proceedings ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Engineering in Medicine and Biology Society Annual Conference (cm), 6442–5 (2012). doi: 10.1109/EMBC.2012.6347469
  10. 10.
    Cernea, D., Olech, P.s., Ebert, A., Kerren, A.: Controlling In-Vehicle Systems with a commercial EEG headset: performance and cognitive load. OpenAccess Series in Informatics pp. 113–122 (2012). doi: 10.4230/OASIcs.VLUDS.2011.113
  11. 11.
    Lin, C.T., Tsai, S.F., Ko, L.W.: EEG-based learning system for online motion sickness level estimation in a dynamic vehicle environment. IEEE Trans. Neural Netw. Learn. Syst. 24(10), 1689–1700 (2013). doi: 10.1109/TNNLS.2013.2275003 CrossRefGoogle Scholar
  12. 12.
    Mercep, L., Spiegelberg, G., Knoll, A.: Reducing the impact of vibration-caused artifacts in a brain-computer interface using gyroscope data. In: Eurocon 2013, July, pp. 1753–1756 (2013). doi: 10.1109/EUROCON.2013.6625214
  13. 13.
    Dijksterhuis, C., de Waard, D., Brookhuis, K.A., Mulder, B.L.J.M., de Jong, R.: Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns. Front. Neurosci. 7(7 AUG), 1–11 (2013). doi: 10.3389/fnins.2013.00149 Google Scholar
  14. 14.
    Kim, I.H., Kim, J.W., Haufe, S., Lee, S.W.: Detection of multi-class emergency situations during simulated driving from ERP. In: 2013 International Winter Work. Brain–Computer Interface, BCI 2013, pp. 49–51 (2013). doi: 10.1109/IWW-BCI.2013.6506626
  15. 15.
    Kim, J.W., Kim, I.H., Lee, S.W.: Neuro-driving: Automatic perception technique for upcoming emergency situations. In: 2013 International Winter Work. Brain–Computer Interface, BCI 2013, pp. 8–9 (2013). doi: 10.1109/IWW-BCI.2013.6506609
  16. 16.
    Kim, J.W., Kim, I.H., Lee, S.W.: Detection of braking intention during simulated driving based on EEG analysis: online study. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 887–891 (2015). doi: 10.1109/SMC.2015.163
  17. 17.
    Kim, I.H., Kim, J.W., Haufe, S., Lee, S.W.: Detection of braking intention in diverse situations during simulated driving based on EEG feature combination. J. Neural Eng. 12(1), 016,001 (2015). doi: 10.1088/1741-2560/12/1/016001 CrossRefGoogle Scholar
  18. 18.
    Liu, Y.T., Lin, Y.Y., Wu, S.L., Chuang, C.H., Lin, C.T.: Brain dynamics in predicting driving fatigue using a recurrent self-evolving fuzzy neural network. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 347–360 (2015). doi: 10.1109/TNNLS.2015.2496330 CrossRefGoogle Scholar
  19. 19.
    Khan, M.J., Hong, K.S.: Passive BCI based on drowsiness detection: an fNIRS study. Biomed. Opt. Express 6(10), 4063 (2015). doi: 10.1364/BOE.6.004063
  20. 20.
    Dehzangi, O., Williams, C.: Towards multi-modal wearable driver monitoring: impact of road condition on driver distraction. In: Proceedings of the 2015 IEEE International Conference on Body Sensor Networks, pp. 1–6 (2015). doi: 10.1109/BSN.2015.7299408
  21. 21.
    Shende, P.M., Jabade, V.S.: Literature review of brain computer interface (BCI) using electroencephalogram signal. In: 2015 International Conference on Pervasive Computing, Advanced Communication Technology and Application for Society, ICPC 2015 (c), pp. 3–7 (2015). doi: 10.1109/PERVASIVE.2015.7087109
  22. 22.
    Tan, D., Nijholt, A.: Brain-computer interfaces. In: Tan, D.S., Nijholt, A. (eds.) Human-Computer Interaction, Chapter. Brain–Computer, Ser., 1 edn., pp. 3–19. Springer, London (2010). doi: 10.1007/978-1-84996-272-8
  23. 23.
    Gürkök, H., Nijholt, A.: BrainComputer Interfaces for Multimodal Interaction: A Survey and Principles. Int. J. Hum. Comput. Interact. 28(5), 292–307 (2012). doi: 10.1080/10447318.2011.582022 CrossRefGoogle Scholar
  24. 24.
    Abdulkader, S.N., Atia, A., Mostafa, M.S.M.: Brain computer interfacing: Applications and challenges. Egypt. Informatics J. 16(2), 213–230 (2015). doi: 10.1016/j.eij.2015.06.002 CrossRefGoogle Scholar
  25. 25.
    George, L., Lécuyer, A.: An overview of research on passive brain-computer interfaces for implicit human-computer interaction. Int. Conf. Appl. Bionics Biomech. ICABB 2010 (2010)Google Scholar
  26. 26.
    Amiri, S., Fazel-Rezai, R., Asadpour, V.: A review of hybrid brain-computer interface systems. Adv. Hum. Comput. Interact. (2013). doi: 10.1155/2013/187024 Google Scholar
  27. 27.
    Fan, Xa, Bi, L., Teng, T., Ding, H., Liu, Y.: A brain–computer interface-based vehicle destination selection system using P300 and SSVEP signals. IEEE Trans. Intell. Transp. Syst 16(1), 274–283 (2015). doi: 10.1109/TITS.2014.2330000 CrossRefGoogle Scholar
  28. 28.
    Izquierdo-Reyes, J., Ramirez-Mendoza, R.A., Bustamante-Bello, M.R., Navarro-Tuch, S., Avila-Vazquez, R.: Advanced driver monitoring for assistance system (ADMAS). Int. J. Interact. Des. Manuf. (2016). doi: 10.1007/s12008-016-0349-9 Google Scholar
  29. 29.
    Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4–5), 411–430 (2000). doi: 10.1016/S0893-6080(00)00026-5 CrossRefGoogle Scholar
  30. 30.
    Bell, A.J., Sejnowski, T.J.: Information-maximization approach to blind separation and blind deconvolution. Technology 1159(6), 1129–1159 (1995). doi: 10.1162/neco.1995.7.6.1129 Google Scholar
  31. 31.
    Wu, R.C.W.R.C., Liang, S.F.L.S.F., Lin, C.T.L.C.T., Hsu, C.F.H.C.F.: Applications of event-related-potential-based brain computer interface to intelligent transportation systems. IEEE Int. Conf. Netw. Sens. Control. 2, 813–818 (2004). doi: 10.1109/ICNSC.2004.1297051 Google Scholar

Copyright information

© Springer-Verlag France 2017

Authors and Affiliations

  1. 1.Escuela de Ingenieria y CienciasTecnologico de MonterreyMexico CityMexico

Personalised recommendations