http://iet.metastore.ingenta.com
1887

Ensemble classifier for driver's fatigue detection based on a single EEG channel

Ensemble classifier for driver's fatigue detection based on a single EEG channel

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Driver's fatigue detection, based on electroencephalography (EEG) signals, is a worthy field of research to study evidence regarding how to exactly pre-warn and avoid casualties nowadays. In this study, an EEG-based system of perfect performance and good stability for evaluating driver's fatigue with only one electrode by ensemble learning method is proposed. Given that EEG signals are unstable and non-linear that using several common entropy measurements to analyse EEG signals is more appropriate including spectral entropy, approximate entropy, sample entropy and fuzzy entropy. In this study, unlike other methods using a single classifier, three ensemble approaches (bagging, random forest and boosting) based on three base classifiers were employed and compared. A driving simulator in this study was used for 12 healthy and adult subjects to perform a continuous simulated driving experiment for 1–2 h. The experimental results show that the proposed method can make use of only one electrode (T6) by gradient boosted DT for driver's fatigue detection, while the average classification accuracy is >94%. The findings of this study indicated that a single EEG channel with optimal ensemble classifier may be a good candidate for usage in the portable system for driver's fatigue detection.

References

    1. 1)
      • 1. Lal, S.K.L., Craig, A.: ‘A critical review of the psychophysiology of driver's fatigue’, Biol. Psychol., 2001, 55, pp. 173194.
    2. 2)
      • 2. Saini, V., Saini, R.: ‘Driver drowsiness detection system and techniques: a review’, Comput. Sci. Inf. Technol., 2014, 5, (3), pp. 42454249.
    3. 3)
      • 3. Dong, Y., Hu, Z., Uchimura, K., et al: ‘Driver inattention monitoring system for intelligent vehicles: a review’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (2), pp. 596614.
    4. 4)
      • 4. Sahayadhas, A., Sundaraj, K., Murugappan, M.: ‘Detecting driver drowsiness based on sensors: a review’, Sensors, 2012, 12, (12), pp. 1693716953.
    5. 5)
      • 5. Jo, J., Lee, S.J., Park, K.R., et al: ‘Detecting driver drowsiness using feature-level fusion and user-specific classification’, Expert Syst. Appl., 2014, 41, (4), pp. 11391152.
    6. 6)
      • 6. Lin, L.Z., Huang, C., Ni, X.P., et al: ‘Driver's fatigue detection based on eye state’, Technol. Health Care, 2015, 23, pp. S453S463.
    7. 7)
      • 7. Fu, R.R., Wang, H.: ‘Detection of driver's fatigue by using noncontact EMG and ECG signals measurement system’, Int. J. Neural Syst., 2014, 24, (24), pp. 478491.
    8. 8)
      • 8. Ma, J.X., Shi, L.C., Lu, B.L.: ‘An EOG-based vigilance estimation method applied for driver fatigue detection’, Neurosci. Biomed. Eng., 2014, 2, (1), pp. 4151.
    9. 9)
      • 9. Kiranyaz, S., Ince, T., Gabbouj, M.: ‘Real-time patient-specific ECG classification by 1D convolutional neural networks’, IEEE Trans. Biomed. Eng., 2016, 63, (3), pp. 664675.
    10. 10)
      • 10. Correa, A.G., Orosco, L., Laciar, E.: ‘Automatic detection of drowsiness in EEG records based on multimodal analysis’, Med. Eng. Phys., 2014, 36, (2), pp. 244249.
    11. 11)
      • 11. Mu, Z.D., Hu, J.F., Yin, J.H.: ‘Driving fatigue detecting based on EEG signals of forehead area’, Int. J. Pattern Recognit. Artif. Intell., 2016, 31, (05), pp. 4044.
    12. 12)
      • 12. Hu, J.F.: ‘Comparison of different features and classifiers for driver fatigue detection based on a single EEG channel’, Comput. Math. Methods Med., 2017, 3, pp. 19, doi: 10.1155/2017/5109530.
    13. 13)
      • 13. Kar, S., Bhagat, M., Routray, A.: ‘EEG signal analysis for the assessment and quantification of driver's fatigue’, Transp. Res. F, Traffic Psychol. Behav., 2010, 13, (5), pp. 297306.
    14. 14)
      • 14. Apker, G., Lance, B., Kerick, S., et al: ‘Combined linear regression and quadratic classification approach for an EEG-based prediction of driver performance’. Int. Conf. Augmented Cognition, Berlin, Heidelberg, 2013, vol. 2, pp. 3140.
    15. 15)
      • 15. Zhao, C., Zhang, X., Zhang, B.: ‘Driver's fatigue expressions recognition by combined features from pyramid histogram of oriented gradient and contourlet transform with random subspace ensembles’, IET Intell. Transp. Syst., 2013, 7, (1), pp. 3645.
    16. 16)
      • 16. Chai, R., Naik, G., Nguyen, T.N., et al: ‘Driver's fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system’, IEEE. J. Biomed. Health Inf., 2016, 21, (3), pp. 715724.
    17. 17)
      • 17. Hu, J.F.: ‘Automated detection of driver fatigue based on AdaBoost classifier with EEG signals’, Front. Comput. Neurosci., 2017, 11, (72), pp. 1172.
    18. 18)
      • 18. Zhang, J.Y., Qiu, W.W., Fu, H.J.: ‘Review of techniques for driver fatigue detection’, Appl. Mech. Mater., 2013, 433–435, pp. 928931.
    19. 19)
      • 19. Fu, R.R., Wang, H., Zhao, W.B.: ‘Dynamic driver's fatigue detection using hidden Markov model in real driving condition’, Expert Syst. Appl., 2016, 63, pp. 397411.
    20. 20)
      • 20. Li, W., He, Q.C., Fan, X.M., et al: ‘Evaluation of driver's fatigue on two channels of EEG data’, Neurosci. Lett., 2012, 506, (2), pp. 235239.
    21. 21)
      • 21. Xiong, Y., Gao, J., Yang, Y., et al: ‘Classifying driving fatigue based on combined entropy measure using EEG signals’, Int. J. Control Autom., 2016, 9, (3), pp. 329338.
    22. 22)
      • 22. Chai, R., Naik, G., Nguyen, T.N., et al: ‘Driver's fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system’, IEEE. J. Biomed. Health Inf., 2016, 99, p. 1.
    23. 23)
      • 23. Rosario, H.D., Solaz, J.S., Rodriguez, N.: ‘Controlled inducement and measurement of drowsiness in a driving simulator’, IET Intell. Transp. Syst., 2010, 4, (4), pp. 280288.
    24. 24)
      • 24. Hassan, A.R., Subasi, A.: ‘Automatic identification of epileptic seizures from EEG signals using linear programming boosting’, Comput. Methods Programs Biomed., 2016, 136, pp. 6577.
    25. 25)
      • 25. Yang, T., Chen, W.T., Cao, G.T.: ‘Automated classification of neonatal amplitude-integrated EEG based on gradient boosting method’, Biomed. Signal Process. Control, 2016, 28, pp. 5057.
    26. 26)
      • 26. Min, J.L., Wang, P., Hu, J.F.: ‘Driver's fatigue detection through multiple entropy fusion analysis in an EEG-based system’, PLOS One, 2017, 12, (12), p. e0188756, doi: 10.1371/journal.pone.0188756.
    27. 27)
      • 27. Lee, K.A., Hicks, G., Nino-Murcia, G.: ‘Validity and reliability of a scale to assess fatigue’, Psychiatry Res., 1991, 36, (3), pp. 291298.
    28. 28)
      • 28. Borg, G.: ‘Psychophysical scaling with applications in physical work and the perception of exertion’, Scand. J. Work Environ. Health, 1990, 16, pp. 5558.
    29. 29)
      • 29. Mu, Z.D., Hu, J.F., Min, J.L.: ‘Driver fatigue detection system using electroencephalography signals based on combined entropy features’, Appl. Sci., 2017, 7, (2), p. 150, doi: 10.3390/app7020150.
    30. 30)
      • 30. Hu, J., Wang, P.: ‘Noise robustness analysis of performance for EEG-based driver's fatigue detection using different entropy feature sets’, Entropy, 2017, 19, (8), pp. 385414, doi: 10.3390/e19080385.
    31. 31)
      • 31. Azarnoosh, M., Nasrabadi, A.M., Mohammadi, M.R., et al: ‘Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: symbolic dynamics’, Chaos Solitons Fractals, 2011, 44, (12), pp. 10541062.
    32. 32)
      • 32. Hu, J.F.: ‘An approach to EEG-based gender recognition using entropy measurement methods’, Knowl.-Based Syst., 2017, 140, pp. 134141, doi: 10.1016/j.knosys.2017.10.032.
    33. 33)
      • 33. Yentes, J.M., Hunt, N., Schmid, K.K., et al: ‘The appropriate use of approximate entropy and sample entropy with short data sets’, Ann. Biomed. Eng., 2013, 41, (2), pp. 349365.
    34. 34)
      • 34. Gajera, V., Shubham, R.: ‘An effective multi-objective task scheduling algorithm using min–max normalization in cloud computing’. Int. Conf. Applied and Theoretical Computing and Communication Technology, India, Bangalore, 2017, pp. 812816.
    35. 35)
      • 35. Li, X., Hu, B., Sun, S., et al: ‘EEG-based mild depressive detection using feature selection methods and classifiers’, Comput. Methods Programs Biomed., 2016, 136, pp. 151161.
    36. 36)
      • 36. Mu, Z.D., Hu, J.F., Min, J.L., et al: ‘Comparison of different entropy as feature for person authentication based on EEG signals’, IET Biometrics, 2017, 6, (6), pp. 409417, doi: 10.1049/iet-bmt.2016.0144.
    37. 37)
      • 37. Polat, K., Güneş, S.: ‘Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform’, Appl. Math. Comput., 2007, 187, (2), pp. 10171026.
    38. 38)
      • 38. Breiman, L.: ‘Bagging predictors’, Mach. Learn., 1996, 24, (2), pp. 123140.
    39. 39)
      • 39. Breiman, L.: ‘Heuristics of instability and stabilization in model selection’, Ann. Stat., 1996, 24, (6), pp. 23502383.
    40. 40)
      • 40. Fraiwan, L., Lweesy, K., Khasawneh, N., et al: ‘Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier’, Comput. Methods Programs Biomed., 2012, 108, (1), pp. 1019.
    41. 41)
      • 41. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, (1), pp. 532.
    42. 42)
      • 42. Friedman, J.H.: ‘Greedy function approximation: a gradient boosting machine’, Ann. Stat., 2001, 29, (5), pp. 11891232.
    43. 43)
      • 43. Hastie, T., Tibshirani, R., Friedman, J.H.: ‘Elements of statistical learning ed’, vol. 2 (Springer, New York, NY, USA, 2009).
    44. 44)
      • 44. Azar, A.T., El-Said, S.A.: ‘Performance analysis of support vector machines classifiers in breast cancer mammography recognition’, Neural Comput. Appl., 2014, 24, (5), pp. 11631177.
    45. 45)
      • 45. Dietterich, T.G.: ‘Machine learning research – four current directions’, AI Mag., 1997, 18, (4), pp. 97136.
    46. 46)
      • 46. Bay, S.: ‘Nearest neighbor classification from multiple feature subsets’, Intell. Data Anal., 1999, 3, (3), pp. 191209.
    47. 47)
      • 47. Dietterich, T.G.: ‘An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization’, Mach. Learn., 2000, 40, (2), pp. 139157.
    48. 48)
      • 48. Bauer, E., Kohavi, R.: ‘An empirical comparison of voting classification algorithms: bagging, boosting, and variants’, Mach. Learn., 1999, 36, (1–2), pp. 105139.
    49. 49)
      • 49. Sun, S.L., Zhang, C.S., Zhang, D.: ‘An experimental evaluation of ensemble methods for EEG signal classification’, Pattern Recognit. Lett., 2007, 28, pp. 21572163.
    50. 50)
      • 50. Hu, J.F., Mu, Z.D., Wang, P.: ‘Multi-feature authentication system based on event evoked electroencephalogram’, J. Med. Imaging Health Inform., 2015, 5, (4), pp. 862870.
    51. 51)
      • 51. Khushaba, R.N., Kodagoda, S., Lal, S., et al: ‘Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm’, IEEE Trans. Biomed. Eng., 2011, 58, pp. 121131.
    52. 52)
      • 52. Mu, Z.D., Hu, J.F., Min, J.L.: ‘EEG-based person authentication using a fuzzy entropy-related approach with two electrodes’, Entropy, 2016, 18, (12), pp. 432438.
    53. 53)
      • 53. Hu, J.F., Min, J.L.: ‘Automated detection of driver's fatigue based on EEG signals using gradient boosting decision tree model’, Cogn. Neurodyn., 2018, 12, (4), pp. 110.
    54. 54)
      • 54. Meunier, D., Lambiotte, R., Fornito, A., et al: ‘Hierarchical modularity in human brain functional networks’, Frontiers in Neuroinformatics, 2009, 3, (37), pp. 112.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5290
Loading

Related content

content/journals/10.1049/iet-its.2018.5290
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address