Open Access Open Access  Restricted Access Subscription or Fee Access

Real Time Fatigue-Driver Detection from Electroencephalography Using Emotiv EPOC+


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v11i3.8562

Abstract


The fatigue driving detection has been developed with many kinds of approaches, such as video using face expressions and Electroencephalography (EEG) that uses the brainwave signals of the driver. This paper proposes a method to implement the driving fatigue detection in real time using Python and Emotiv EPOC+ with 14 channels. The EEG recorded database will extract their features per-30 seconds. The prediction process gets the EEG recorded data from the driver doing the driving simulation and trains it using the extracted features data from the database. The results print as Fit and Alert, or Fatigue and Sleepy. The contributions of the authors in this paper are as follows: i) the reduction of the processing time, such as reading input and output files and communicating among different programming languages; ii) the analysis and comparison of the dynamics of prediction results and significant channels from the results of the previous research, and iii) the development of the system from semi real-time to real-time forecasting.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


Driver Fatigue Detection; Electroencephalogram (EEG); Find Significant Channels

Full Text:

PDF


References


F. Wang, J. Lin, W. Wang and H. Wang, EEG-Based Mental Fatigue Assessment During Driving by Using Sample Entropy and Rhythm Energy, (2015) Cyber Technology in Automation, Control and Intelligent Systems, pp. 1906 - 1911.
http://dx.doi.org/10.1109/cyber.2015.7288238

M. A. Li, An EEG-Based Method For Detecting Drowsy Driving State, Fuzzy Systems and Knowledge Discovery (FSKD), 2010, pp. 2164 - 2167.
http://dx.doi.org/ 10.1109/FSKD.2010.5569757

B. C. Chang, J. E. Lim, H. J. Kim and B. H. Seo, A Study of Classification of The Level of Sleepiness For The Drowsy Driving Prevention, (2007) SICE, pp. 3084 - 3089.
http://dx.doi.org/10.1109/sice.2007.4421521

R. Ahmed, K. Emon and M. Hossain, Robust Driver Fatigue Recognition Using Image Processing, (2014) Informatics, Electronics & Vision (ICIEV), pp. 1-6.
http://dx.doi.org/10.1109/iciev.2014.6850713

K. Rezaee, H. Sabzevari, S. Alavi, M. Madanian, M. Rasegh Ghezelbash, H. Khavari and J. Haddadnia, Real-time Intelligent Alarm System of Driver Fatigue Based On Video Sequences, (2013) Robotics and Mechatronics (ICRoM), pp. 378-383.
http://dx.doi.org/10.1109/icrom.2013.6510137

J. He, D. Liu, Z. Wan and C. Hu, A Noninvasive Real-Time Driving Fatigue Detection Technology Based On Left Prefrontal Attention and Meditation EEG, (2014) Multisensor Fusion and Information Integration for Intelligent Systems (MFI), pp. 1-6.
http://dx.doi.org/10.1109/mfi.2014.6997673

B. T. Nugraha, R. Sarno, D. A. Asfani, T. Igasaki and M. N. Munawar, Classification of Driving Fatigue State Based On EEG Using Emotiv EPOC+, (2016) Journal of Theoretical and Applied Information Technology, 86(3), pp. 1-14.

R. Sarno, B. A. Sanjoyo, I. Mukhlash and H. M. Astuti, Petri Net Model of ERP Business Process Variation for Small and Medium Enterprises, (2013) Journal of Theoretical and Applied Information Technology, 51(1), pp. 31-38.

R. Sarno, C. A. Djeni, I. Mukhlash and D. Sunaryono, Developing a Workflow Management System for Enterprise Resource Planning, (2015) Journal of Theoretical and Applied Information Technology, 72(3), pp. 412-421.

R. Sarno, R. D. Dewandono, T. Ahmad, M. F. Naufal and F. Sinaga, Hybrid Association Rule Learning and Process Mining for Fraud Detection, (2015) IAENG International Journal of Computer Science, 42(2),pp. 59-72,.

A. H. Basori, R. Sarno and S. Widyanto, The Development of 3D Multiplayer Mobile Racing Games Based On 3D Photo Satellite Map, (2008) Proceedings of the IASTED International Conference on Wireless and Optical Communications, pp. 1-5.
http://dx.doi.org/10.1109/wocn.2008.4542540

R. Sarno, P. Sari, H. Ginardi dan D. Sunaryono, Decision Mining For Multi Choice Workflow Patterns, (2013) Computer, Control, Informatics and Its Applications (IC3INA), pp. 337 - 342.
http://dx.doi.org/10.1109/ic3ina.2013.6819197

Arcady A. Putilov, , Olga G. Donskaya, Construction and validation of the EEG analogues of the Karolinska sleepiness scale based on the Karolinska drowsiness test, (2013) Clinical Neurophysiology, 124(7), pp. 1346–1352.
http://dx.doi.org/10.1016/j.clinph.2013.01.018

R. Nowak, Optimal Signal Estimation Using Cross-Validation, (1997) Signal Processing Letters, IEEE, 4 (1), pp. 23-25.
http://dx.doi.org/10.1109/97.551692

M. Brass, M. Ullsperger, T. Knoesche, D. Cramon and N. Phillips, Who Comes First? The Role of the Prefrontal and Parietal Cortex in Cognitive Control, (2005) Cognitive Neuroscience, 17(9), pp. 1367 - 1375.
http://dx.doi.org/10.1162/0898929054985400

J. Fuster, The Prefrontal Cortex Makes the Brain a Preadaptive System, (2014) Proceedings of the IEEE, 102(4), pp. 417 - 426.
http://dx.doi.org/10.1109/jproc.2014.2306250

S. Galetta, Occipital Lobe, (2014) Encyclopedia of the Neurological Sciences (Second Edition), pp. 626–632.
http://dx.doi.org/10.1016/B978-0-12-385157-4.01166-0

E. B. Johnson, E. M. Rees, I. Labuschagne, A. Durr, B. R. Leavitt, R. A. Roos, R. Reilmann, H. Johnson, N. Z. Hobbs, D. R. Langbehn, J. C. Stout, S. J. Tabrizi and R. I. Scahill, The impact of occipital lobe cortical thickness on cognitive task performance: An investigation in Huntington's Disease, (2015) Neuropsychologia, vol. 79, pp. 138–146.
http://dx.doi.org/10.1016/j.neuropsychologia.2015.10.033

O. Braddick, Occipital Lobe (Visual Cortex): Functional Aspects, (2015) International Encyclopedia of the Social & Behavioral Sciences (Second Edition), pp. 127–132.
http://dx.doi.org/10.1016/b978-0-08-097086-8.55041-7


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize