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International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2018: Bioinformatics and Biomedical Engineering pp 129–138Cite as

Discrimination Between Normal Driving and Braking Intention from Driver’s Brain Signals

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10813))

Abstract

Advanced driver-assistance systems (ADAS) are in-car technologies that record and process vehicle and road information to take actions to reduce the risk of collision. These technologies however do not use information obtained directly from the driver such as the brain activity. This work proposes the recognition of brake intention using driver’s electroencephalographic (EEG) signals recorded in real driving situations. Five volunteers participated in an experiment that consisted on driving a car and braking in response to a visual stimulus. Driver’s EEG signals were collected and employed to assess two classification scenarios, pre-stimulus vs pos-stimulus and no-braking vs brake-intention. Classification results showed across-all-participants accuracies of 85.2 ± 5.7% and 79 ± 9.1%, respectively, which are above the chance level. Further analysis on the second scenario showed that true positive rate (77.1%) and true negative rate (79.3%) were very similar, which indicates no bias in the classification between no-braking vs brake-intention. These results show that driver’s EEG signals can be used to detect brake intention, which could be useful to take actions to avoid potential collisions.

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References

  1. WHO: Global status report on road safety 2015. World Health Organization (2015)

    Google Scholar 

  2. Louwerse, W.J.R., Hoogendoorn, S.P.: ADAS safety impacts on rural and urban highways, pp. 887–890, June 2004

    Google Scholar 

  3. Khan, J.: Using ADAS sensors in implementation of novel automotive features for increased safety and guidance, pp. 753–758, February 2016

    Google Scholar 

  4. Okuda, R., Kajiwara, Y., Terashima, K.: A survey of technical trend of ADAS and autonomous driving, pp. 1–4, April 2014

    Google Scholar 

  5. Stanton, N.A., Marsden, P.: From fly-by-wire to drive-by-wire: safety implications of automation in vehicles. Saf. Sci. 24, 35–49 (1996)

    Article  Google Scholar 

  6. Young, M.S., Stanton, N.A., Harris, D.: Driving automation: learning from aviation about design philosophies. Int. J. Veh. Des. 45, 323–338 (2007)

    Article  Google Scholar 

  7. Haufe, S., Treder, M., Gugler, M.F., Sagebaum, M., Curio, G., Blankertz, B.: EEG potentials predict upcoming emergency brakings during simulated driving. J. Neural Eng. 8, 056001 (2011)

    Article  Google Scholar 

  8. 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, 016001 (2015)

    Article  Google Scholar 

  9. Khaliliardali, Z., Chavarriaga, R., Gheorghe, L.A., del Millan, J.R.: Detection of anticipatory brain potentials during car driving. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3829–3832 (2012)

    Google Scholar 

  10. Haufe, S., Kim, J.W., Kim, I.H., Sonnleitner, A., Schrauf, M., Curio, G., Blankert, B.: Electrophysiology-based detection of emergency braking intention in real-world driving. J. Neural Eng. 11, 056011 (2014)

    Article  Google Scholar 

  11. Khaliliardali, Z., Chavarriaga Lozano, R., Zhang, H., Gheorghe, L.A., Millán, J.R.: Single trial classification of neural correlates of anticipatory behavior during real car driving (2016)

    Google Scholar 

  12. WHO: World medical association declaration of Helsinki. Bull. World Health Organ. 79, 373–374 (2001)

    Google Scholar 

  13. ISO 15765-3:2004: Road vehicles - diagnostics on Controller Area Networks (CAN) - Part 4: Requirements for emissions-related systems. Standard, International Organization for Standardization, Geneva, January 2005

    Google Scholar 

  14. Elm Electronics Inc.: ELM327 OBD to RS232 Intepreter (2014)

    Google Scholar 

  15. g.Tech: g.Nautilus wireless biosignal acquisition. http://www.gtec.at/Products/Hardware-and-Accessories/g.Nautilus-Specs-Features. Accessed 30 Sept 16

  16. Venkataramanan, S., Prabhat, P., Choudhury, S.R., Nemade, H.B., Sahambi, J.S.: Biomedical instrumentation based on electrooculogram (EOG) signal processing and application to a hospital alarm system. In: Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, pp. 535–540, January 2005

    Google Scholar 

  17. OBD Solutions : Multiprotocol OBD to UART Interpreter. STN1110 datasheet, November 2012

    Google Scholar 

  18. Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.M.: FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 1:1–1:9 (2011)

    Article  Google Scholar 

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Acknowledgments

This research has been funded by the National Council of Science and Technology of Mexico (CONACyT) through grants 268958 and PN2015-873. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.

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Authors and Affiliations

  1. Tecnológico de Monterrey en Guadalajara, Av. Gral Ramón Corona 2514, 45201, Zapopan, Jalisco, Mexico

    Efraín Martínez, Luis Guillermo Hernández & Javier Mauricio Antelis

Authors
  1. Efraín Martínez
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  2. Luis Guillermo Hernández
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  3. Javier Mauricio Antelis
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Corresponding author

Correspondence to Javier Mauricio Antelis .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Francisco Ortuño

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Martínez, E., Hernández, L.G., Antelis, J.M. (2018). Discrimination Between Normal Driving and Braking Intention from Driver’s Brain Signals. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-78723-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78722-0

  • Online ISBN: 978-3-319-78723-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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