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An Automatic Driver Assistant Based on Intention Detecting Using EEG Signal

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 666)

Abstract

Each year, vehicle safety is increasing. Recently brain signals were used to assist drivers. Attempting to do movement produces electrical signals in specific regions of the brain. We developed a system based on motor intention to assist drivers and prevent car accidents. The main objective of this work is improving reaction time to external hazards. The motor intention was recorded by 16 channels of a portable device called Open-BCI. Extracting features was done by common spatial patterns which is a well-known method in motor imagery based brain computer interface (BCI) systems. By using enhanced common spatial pattern (CSP) called strong uncorrelated transform complex common spatial pattern (SUTCCSP), features of preprocessed data were extracted. Regarding the nonlinear nature of electroencephalogram (EEG), support vector machine (SVM) with kernel trick classifier was used to classify features into 3 classes: left, right and brake. Due to using developed SVM, commands can be predicted 500 ms earlier with the system accuracy of 94.6% on average.

Keywords

  • Intentional EEG
  • Driving assistant
  • BCI

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(Baker et al. [14])

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References

  1. Shaout A, Colella D, Awad S (2011) Advanced driver assistance systems - past, present and future. In: 2011 seventh international computer engineering conference (ICENCO 2011), pp 72–82, Giza

    Google Scholar 

  2. Liu CC, Hosking SG, Lenné MG (2009) Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Saf Res 40(4):239–245

    CrossRef  Google Scholar 

  3. Sigari MH, Fathy M, Soryani M (2013) A driver face monitoring system for fatigue and distraction detection. Int J Veh Technol 2013:1–11

    CrossRef  Google Scholar 

  4. Sherk H, Fowler GA (2001) Chapter 16 Neural analysis of visual information during locomotion. Prog Brain Res 134:247–264

    CrossRef  Google Scholar 

  5. Haufe S, Kim JW, Kim IH, Sonnleitner A, Schrauf M, Curio G, Blankertz B (2014) Electrophysiology-based detection of emergency braking intention in real-world driving. J Neural Eng 11(5):056011

    CrossRef  Google Scholar 

  6. Kim IH, Kim JW, Haufe S, Lee SW (2014) Detection of braking intention in diverse situations during simulated driving based on EEG feature combination. J Neural Eng 12(1):016001

    CrossRef  Google Scholar 

  7. Borghini G, Astolfi L, Vecchiato G, Mattia D, Babiloni F (2014) Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev 44:58–75

    CrossRef  Google Scholar 

  8. Teng T, Bi L (2014) A novel EEG-based detection method of emergency situations for assistive vehicles. In 2017 seventh international conference on information science and technology (ICIST), IEEE, pp 335–339

    Google Scholar 

  9. Alyasseri ZAA, Khader AT, Al-Betar MA, Papa JP, Ahmad Alomari O (2018) EEG-based person authentication using multi-objective flower pollination algorithm. In: 2018 IEEE congress on evolutionary computation (CEC), IEEE, pp. 1–8

    Google Scholar 

  10. Nguyen T, Hettiarachchi I, Khatami A, Gordon-Brown L, Lim CP, Nahavandi S (2018) Classification of multi-class BCI data by common spatial pattern and fuzzy system. IEEE Access 6:27873–27884

    CrossRef  Google Scholar 

  11. Kim Y, Park C (2015) Strong uncorrelated transform applied to spatially distant channel EEG data. IEIE Tran Smart Process Comput 4(2):97–102

    CrossRef  Google Scholar 

  12. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  13. Aizerman MA (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 25:821–837

    MATH  Google Scholar 

  14. Baker M, Akrofi K, Schiffer R, O’Boyle MW (2008) EEG patterns in mild cognitive impairment (MCI) patients. Open Neuroimaging J 2:52

    CrossRef  Google Scholar 

  15. Cho H, Ahn M, Ahn S, Kwon M, Jun SC (2017) EEG datasets for motor imagery brain-computer interface. Gigascience 1(7):1–8

    Google Scholar 

  16. Hoffman LD, Polich J (1998) EEG, ERPs and food consumption. Biol Psychol 48(2):139–151

    CrossRef  Google Scholar 

  17. Falzon O, Camilleri KP, Muscat J (2010) Complex-valued spatial filters for task discrimination. In 2010 annual international conference of the IEEE engineering in medicine and biology, IEEE, pp 4707–4710

    Google Scholar 

  18. Park C, Took CC, Mandic DP (2013) Augmented complex common spatial patterns for classification of noncircular EEG from motor imagery tasks. IEEE Trans Neural Syst Rehabil Eng 22(1):1–10

    CrossRef  Google Scholar 

  19. Kim Y, Ryu J, Kim KK, Took CC, Mandic DP, Park C (2016) Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Comput Intell Neurosci 2016:1

    Google Scholar 

  20. Li X, Chen X, Yan Y, Wei W, Wang ZJ (2014) Classification of EEG signals using a multiple kernel learning support vector machine. Sensors 14(7):12784–12802

    CrossRef  Google Scholar 

  21. Garcia GN, Ebrahimi T, Vesin JM (2003) Support vector EEG classification in the Fourier and time-frequency correlation domains. In: First international IEEE EMBS conference on neural engineering, 2003, conference proceedings, IEEE, pp 591–594

    Google Scholar 

Download references

Acknowledgement

The authors appreciate those who contributed to make this research successful. This research is supported by Center for Research and Innovation (PPPI) and Faculty of Engineering, Universiti Malaysia Sabah (UMS) under the Research Grant (SBK0393-2018).

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Correspondence to Ali Farzamnia .

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Gougeh, R.A., Rezaii, T.Y., Farzamnia, A. (2021). An Automatic Driver Assistant Based on Intention Detecting Using EEG Signal. In: , et al. Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 . Lecture Notes in Electrical Engineering, vol 666. Springer, Singapore. https://doi.org/10.1007/978-981-15-5281-6_43

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