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A novel real-time driving fatigue detection system based on wireless dry EEG

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Abstract

Development of techniques for detection of mental fatigue has varied applications in areas where sustaining attention is of critical importance like security and transportation. The objective of this study is to develop a novel real-time driving fatigue detection methodology based on dry Electroencephalographic (EEG) signals. The study has employed two methods in the online detection of mental fatigue: power spectrum density (PSD) and sample entropy (SE). The wavelet packets transform (WPT) method was utilized to obtain the \(\theta \) (4–7 Hz), \(\alpha \) (8–12 Hz) and \(\beta \) (13–30 Hz) bands frequency components for calculating corresponding PSD of the selected channels. In order to improve the fatigue detection performance, the system was individually calibrated for each subject in terms of fatigue-sensitive channels selection. Two fatigue-related indexes: (\(\theta +\alpha \))/\(\beta \) and \(\theta \)/\(\beta \) were computed and then fused into an integrated metric to predict the degree of driving fatigue. In the case of SE extraction, the mean of SE averaged across two EEG channels (‘O1h’ and ‘O2h’) was used for fatigue detection. Ten healthy subjects participated in our study and each of them performed two sessions of simulated driving. In each session, subjects were required to drive simulated car for 90 min without any break. The results demonstrate that our proposed methods are effective for fatigue detection. The prediction of fatigue is consistent with the observation of reaction time that was recorded during simulated driving, which is considered as an objective behavioral measure.

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References

  • Abbasi NI, Bodala IP, Bezerianos A, Sun Y, Al-Nashash H, Thakor NV (2017) Role of multisensory stimuli in vigilance enhancement-a single trial event related potential study. In: 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2017. IEEE, pp 2446–2449

  • Amiri GG, Asadi A (2009) Comparison of different methods of wavelet and wavelet packet transform in processing ground motion records. Int J Civ Eng 7(4):248–257

    Google Scholar 

  • Atchley P, Chan M, Gregersen S (2014) A strategically timed verbal task improves performance and neurophysiological alertness during fatiguing drives. Hum Factors 56(3):453–462

    Article  PubMed  Google Scholar 

  • Ba Y, Zhang W, Wang Q, Zhou R, Ren C (2017) Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system. Transp Res Part C Emerg Technol 74:22–33

    Article  Google Scholar 

  • Charbonnier S, Roy RN, Bonnet S, Campagne A (2016) Eeg index for control operators mental fatigue monitoring using interactions between brain regions. Expert Syst Appl 52:91–98

    Article  Google Scholar 

  • Chew LH, Teo J, Mountstephens J (2016) Aesthetic preference recognition of 3D shapes using eeg. Cogn Neurodyn 10(2):165–173

    Article  PubMed  Google Scholar 

  • Coifman RR, Meyer Y, Quake S, Wickerhauser MV (1994) Signal processing and compression with wavelet packets. In: Byrnes JS, Hargreaves KA, Berry K (eds) Wavelets and their applications. Springer, Dordrecht, pp 363–379

    Google Scholar 

  • Dai Z, de Souza J, Lim J, Ho PM, Chen Y, Li J, Thakor N, Bezerianos A, Sun Y (2017) Eeg cortical connectivity analysis of working memory reveals topological reorganization in theta and alpha bands. Front Hum Neurosci 11(12):783–790

    Google Scholar 

  • Delorme A, Makeig S (2004) Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  PubMed  Google Scholar 

  • Eoh HJ, Chung MK, Kim S-H (2005) Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. Int J Ind Ergon 35(4):307–320

    Article  Google Scholar 

  • Gharagozlou F, Saraji GN, Mazloumi A, Nahvi A, Nasrabadi AM, Foroushani AR, Kheradmand AA, Ashouri M, Samavati M (2015) Detecting driver mental fatigue based on eeg alpha power changes during simulated driving. Iran J Public Health 44(12):1693–1702

    PubMed  PubMed Central  Google Scholar 

  • González-Rodríguez G, Colubi A, Gil MÁ (2012) Fuzzy data treated as functional data: a one-way anova test approach. Comput Stat Data Anal 56(4):943–955

    Article  Google Scholar 

  • Gurudath N, Riley HB (2014) Drowsy driving detection by eeg analysis using wavelet transform and k-means clustering. Procedia Comput Sci 34:400–409

    Article  Google Scholar 

  • Hirvonen K, Puttonen S, Gould K, Korpela J, Koefoed VF, Müller K (2010) Improving the saccade peak velocity measurement for detecting fatigue. J Neurosci Methods 187(2):199–206

    Article  PubMed  Google Scholar 

  • Hu J (2017) Comparison of different features and classifiers for driver fatigue detection based on a single eeg channel. Comput Math Methods Med 9(4):832–843

    Google Scholar 

  • Jap BT, Lal S, Fischer P, Bekiaris E (2009) Using eeg spectral components to assess algorithms for detecting fatigue. Expert Syst Appl 36(2):2352–2359

    Article  Google Scholar 

  • Jo J, Lee SJ, Jung HG, Park KR, Kim J (2011) Vision-based method for detecting driver drowsiness and distraction in driver monitoring system. Opt Eng 50(12):13202–13209

    Article  Google Scholar 

  • Kar S, Bhagat M, Routray A (2010) Eeg signal analysis for the assessment and quantification of drivers fatigue. Transp Res Part F Traffic Psychol Behav 13(5):297–306

    Article  Google Scholar 

  • Khushaba RN, Kodagoda S, Lal S, Dissanayake G (2011) Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans Biomed Eng 58(1):121–131

    Article  PubMed  Google Scholar 

  • LaFleur K, Cassady K, Doud A, Shades K, Rogin E, He B (2013) Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. J Neural Eng 10(4):1308–1326

    Article  Google Scholar 

  • Larman C (2012) Applying UML and patterns: an introduction to object oriented analysis and design and interative development, vol 36. Pearson Education India, London

    Google Scholar 

  • Lee B-G, Lee B-L, Chung W-Y (2014) Mobile healthcare for automatic driving sleep-onset detection using wavelet-based eeg and respiration signals. Sensors 14(10):17915–17936

    Article  PubMed  Google Scholar 

  • Li X, Shang X, Morales-Esteban A, Wang Z (2017) Identifying p phase arrival of weak events: the akaike information criterion picking application based on the empirical mode decomposition. Comput Geosci 100:57–66

    Article  Google Scholar 

  • Liang S, Lin C, Wu R, Chen Y, Huang T, Jung T (2006) Monitoring driver’s alertness based on the driving performance estimation and the eeg power spectrum analysis. In: 27th annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005. IEEE, pp 5738–5741

  • Ludwig KA, Miriani RM, Langhals NB, Joseph MD, Anderson DJ, Kipke DR (2009) Using a common average reference to improve cortical neuron recordings from microelectrode arrays. J Neurophysiol 101(3):1679–1689

    Article  PubMed  Google Scholar 

  • Mugler EM, Ruf CA, Halder S, Bensch M, Kubler A (2010) Design and implementation of a p300-based brain-computer interface for controlling an internet browser. IEEE Trans Neural Syst Rehabil Eng 18(6):599–609

    Article  PubMed  Google Scholar 

  • Myrden A, Chau T (2017) A passive eeg-bci for single-trial detection of changes in mental state. IEEE Trans Neural Syst Rehabil Eng 25(4):345–356

    Article  PubMed  Google Scholar 

  • Panicker R, Puthusserypady S, Sun Y (2011) An asynchronous p300 bci with ssvep-based control state detection. IEEE Trans Biomed Eng 99:1781–1788

    Article  Google Scholar 

  • Rau PS (2005) Drowsy driver detection and warning system for commercial vehicle drivers: field operational test design, data analyses, and progress. In: National Highway Traffic Safety Administration 05–0192

  • Riccio A, Leotta F, Bianchi L, Aloise F, Zickler C, Hoogerwerf EJ, Kubler A, Mattia D, Cincotti F (2011) Workload measurement in a communication application operated through a p300-based brain-computer interface. J Neural Eng 8(2):876–884

    Article  Google Scholar 

  • Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):2039–2049

    Article  Google Scholar 

  • Wang H, Li Y, Long J, Yu T, Gu Z (2014) An asynchronous wheelchair control by hybrid eeg-eog brain-computer interface. Cogn Neurodyn 8(5):399–409

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang H, Zhang C, Shi T, Wang F, Ma S (2015) Real-time eeg-based detection of fatigue driving danger for accident prediction. Int J Neural Syst 25(02):643–651

    Article  Google Scholar 

  • Wang R, Wang J, Yu H, Wei X, Yang C, Deng B (2015) Power spectral density and coherence analysis of alzheimers eeg. Cogn Neurodyn 9(3):291–304

    Article  PubMed  Google Scholar 

  • Williamson A, Lombardi DA, Folkard S, Stutts J, Courtney TK, Connor JL (2011) The link between fatigue and safety. Accid Anal Prev 43(2):498–515

    Article  PubMed  Google Scholar 

  • Wu D, Lawhern VJ, Gordon S, Lance BJ, Lin C-T (2016) Driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression (owarr). IEEE Trans Fuzzy Syst 10(5):1493–1502

    Google Scholar 

  • Zhang C, Wang H, Fu R (2014) Automated detection of driver fatigue based on entropy and complexity measures. IEEE Trans Intell Transp Syst 15(1):168–177

    Article  Google Scholar 

  • Zhang L, Gan JQ, Wang H (2015) Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method. Cogn Neurodyn 9(5):495–508

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhao C, Zheng C, Zhao M, Liu J, Tu Y (2011) Automatic classification of driving mental fatigue with eeg by wavelet packet energy and kpca-svm. Int J Innov Comput Control 7(3):1157–1168

    Google Scholar 

  • Zhao C, Zhao M, Yang Y, Gao J, Rao N, Lin P (2017) The reorganization of human brain networks modulated by driving mental fatigue. IEEE J Biomed Health Inform 21(3):743–755

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Defence Science Organisation (DSO) of Singapore under Grant Number R-719-000-027-592, Technology Development Project of Guangdong Province (No. 2017A010101034), Innovation Projects for Science supported by Department of Education of Guangdong Province (No. 2016KTSCX141), Science Foundation for Young Teachers of Wuyi University (No. 2018td02), Jiangmen Research and Development Program ([2017]268) and the China Scholarship Council ([2016]5113).

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Authors and Affiliations

  1. Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456, Singapore

    Hongtao Wang, Andrei Dragomir, Nida Itrat Abbasi, Junhua Li, Nitish V. Thakor & Anastasios Bezerianos

  2. School of Information Engineering, Wuyi University, Jiangmen, 529020, Guangdong, China

    Hongtao Wang

  3. Department of Biomedical Engineering, National University of Singapore, Singapore, 117456, Singapore

    Nida Itrat Abbasi

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  1. Hongtao Wang
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  2. Andrei Dragomir
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  3. Nida Itrat Abbasi
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  4. Junhua Li
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  5. Nitish V. Thakor
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  6. Anastasios Bezerianos
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Corresponding authors

Correspondence to Junhua Li or Anastasios Bezerianos.

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Wang, H., Dragomir, A., Abbasi, N.I. et al. A novel real-time driving fatigue detection system based on wireless dry EEG. Cogn Neurodyn 12, 365–376 (2018). https://doi.org/10.1007/s11571-018-9481-5

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  • DOI: https://doi.org/10.1007/s11571-018-9481-5

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