Sequential nonlinear encoding: A low dimensional regression algorithm with application to EEG-based driving fatigue detection

Document Type : Article

Authors

Department of Electrical Engineering, Sharif University of Technology, Tehran 11155-8639, Iran

Abstract

We introduce a novel Regularized Kernel Projection Pursuit Regression method which is a two-step nonlinearity encoding algorithm tailored for such very low dimensional problems as fatigue detection. This way, the data nonlinearity can be investigated from two different perspectives, first by transforming the data into a high dimensional intermediate space and then by using their spline estimations to the output variables which allows for a hierarchical unfolding of data. Experimental results on the SEED database shows an average RMSE value of 0.1080% and 0.1054% respectively for the temporal and posterior areas of the brain. Our method is also validated by conducting some experiments on Parkinson's disease prediction which further demonstrate the efficiency of our method for low-dimensional regression problems.Traditional off-the-shelf regression methods like SVR, KSVR, and GLM methods all require their link functions to be previously selected which limits their effectiveness for encoding the nonlinearity of a highly complex low dimensional data set. Moreover, conventional PPR does not deal with the very low dimensionality of data. This paper proposes a novel regression algorithm to address the encoding problem of a highly complex low dimensional data, which is usually encountered in bio-neurological prediction tasks like EEG based driving fatigue detection.

Keywords

References

References:
1. Platte, K., Alleblas, C.C., Inthout, J., et al. \Measuring fatigue and stress in laparoscopic surgery: validity and reliability of the star-track test", Minimally Invasive Therapy & Allied technologies, 1, pp. 1{8 (2018).
2. Ndaro, N.Z. and Wang, S.Y. \Effects of fatigue based on electroencephalog- raphy signal during laparoscopic surgical simulation", Minimally Invasive Surgery, 2018, pp. 1{7 (2018).
3. Amirian, I. \The impact of sleep deprivation on surgeons performance during night shifts", Dan Med J,61, pp. 1{14 (2014).
4. Tuwairqi, K., Selter, J.H., and Sikder, S. \Assessment of surgeon fatigue by surgical imulators", Open Access Surgery, 8, pp. 43{50 (2015).
5. Dehais, F., Dupres, A., Di Flumeri, G., et al. \Monitoring pilots cognitive fatigue with engage- ment features in simulated and actual fNIRS-EEG passive BCI", IEEE SMC (2018).
6. Zhang, L., Zhou, Q., Yin, Q., et al. \Assessment of pilots mental fatigue status with the eye movement features", In International Conference on Applied Human Factors and Ergonomics, Springer, pp. 146{155(2018).
7. Yan, R., Wu, C., and Wang, Y. \Exploration and evaluation of individual difference to driving  fatigue for high-speed railway: a parametric SVM model based on multidimensional visual cue", IET Intelligent Transport Systems, 12(6), pp. 504{512 (2018).
8. Zhou, X., Yao, D., Zhu, M., et al. \Vigilance detection method for high-speed rail using wireless wearable EEG collection technology based on low-rank matrix decomposition", IET Intelligent Transport Systems, 12(8), pp. 819{825 (2018).
9. Liu, N.H., Chiang, C.Y., and Chu, H.C.  Recognizing the degree of human attention using EEG signals from mobile sensors", Sensors, 13(8), pp. 10273{10286 (2013).
10. Barwick, F., Arnett, P., and Slobounov, S. \EEG correlates of fatigue during administration of a neuropsychological test battery", Clinical  nurophysiology,123(2), pp. 278{284 (2012).
11. NHTSA, Fatal Traffic Crash Data. https://www. transportation.gov/briefing-room/nhtsa0117 (2017). [Online; accessed 06-October-2017].
12. Hedlund, L., Gyllensten, A.L., and Hansson, L. \A psychometric study of the multidimensional fatigue inventory to assess fatigue in patients with schizophrenia spectrum disorders", Community Mental Health Journal, 51(3), pp. 377{382 (2015).
13. Meek, P.M., Nail, L.M., Barsevick, A., et al. \Psychometric testing of fatigue instruments for use with cancer patients", Nursing Research, 49(4), pp. 181{190 (2000).
14. Lerdal, A., Wahl, A.K., Rustoen, T., et al. \Fatigue in the general population: a translation and test of the psychometric properties of the Norwegian version of the fatigue severity scale", Scandinavian Journal of Public Health, 33(2), pp. 123{130 (2005).
15. Whitehead, L. \The measurement of fatigue in chronic illness: a systematic review of unidimensional and multidimensional fatigue measures", Journal of Pain and Symptom Management, 37(1), pp. 107{128 (2009).
16. Herlofson, K., Heijnen, C., Lange, J., et al. \Inflammation and fatigue in early, untreated Parkinson's disease", Acta Neurologica Scandinavica, 138(5), pp.394{399 (2018).
17. Galarza, E.E., Egas, F.D., Silva, F.M., et al. \Realtime driver drowsiness detection based on drivers face image behavior using a system of human computer interaction implemented in a smart- phone", In: International Conference on Information heoretic Security, Springer, pp. 563{572 (2018).
18. Jie, Z., Mahmoud, M., Sta ord-Fraser, Q., et al. \Analysis of yawning behaviour in spontaneous expressions of drowsy drivers", In: Automatic Face & Gesture Recognition (FG 2018), 2018, 13th IEEE International Conference on IEEE, pp. 571{576 (2018).
19. Kumar, A. and Patra, R. \Driver drowsiness monitoring system using visual behaviour and machine learning", In: 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE, pp. 339{344 (2018).
20. Sasikala, R., Suresh, S., Chandramohan, J., et al.\Driver drowsiness detection system using image processing technique by the human visual system", International Journal of Emerging Technologies in Engineering Research (IJETER), 6(6), pp. 1{11 (2018).
21. Liu, Y.T., Wu, S.L., Chou, K.P., et al. \Driving fatigue prediction with pre-event elec- troephalography (EEG) via a recurrent fuzzy neural network", In: Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on IEEE, pp. 2488{2494 (2016).
22. McDonald, A.D., Lee, J.D., Schwarz, C., et al. \A contextual and temporal algorithm for driver drowsiness detection", Accident Analysis & Prevention, 113, pp. 25{37 (2018).
23. Dimitrakopoulos, G.N., Kakkos, I., Dai, Z., et al. \Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks", IEEE Transactions on Neural Systems and rehabilitation Engineering, 26(4),pp. 740{749 (2018).
24. Jammes, B., Sharabty, H., and Esteve, D. \Automatic eog analysis: A first step toward automatic drowsiness scoring during wake-sleep transitions", Somnologieschlafforschung und Schlafmedizin, 12(3), pp. 227{232 (2008).
25. Zheng, W.L. and Lu, B.L. \A multimodal approach to estimating vigilance using eeg and forehead eog", Journal of Neural Engineering, 14(2), 026017 (2017).
26. Patel, M., Lal, S.K., Kavanagh, D., et al. \Applying neural network analysis on heart rate variability data to assess driver fatigue", Expert Systems with Applications, 38(6), pp. 7235{7242 (2011).
27. Jung, S.J., Shin, H.S., and Chung, W.Y. \Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel", IET Intelligent Transport Systems, 8(1) pp. 43{50 (2014).
28. Zhao, C., Zhao, M., Liu, J., et al. eltroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator", Accident Analysis & Prevention, 45, pp. 83{90 (2012).
29. Chowdhury, M.E., El Beheri, S.H., Albardawil, M.N., et al. \Driver drowsiness detection study using heart rate variability analysis in virtual reality environment", In: Qatar Foundation Annual Research Conference Proceedings, 2018, HBKU Press Qatar, pp. 11{32 (2018).
30. Lee, J., Kim, J., and Shin, M. \Correlation analysis between electrocardio- graphy (ECG) and photoplethysmogram (PPG) data for drivers drowsiness detection using noise replacement method", Procedia Computer Science, 116, pp. 421{426 (2017).
31. Chieh, T.C., Mustafa, M.M., Hussain, A., et al. \Driver fatigue detection using steering grip force", In: Research and Development, 2003. SCORED 2003. Proceedings. Student Conference on. IEEE, pp. 45{48 (2003).
32. Shi, L.C., Duan, R.N., and Lu, B.L. \A robust principal component analysis algorithm for EEG-based vigilance estimation", In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual international Conference of the IEEE, IEEE, pp. 6623{6626 (2013).
33. Mackiewicz, A. and Ratajczak, W. \Principal components analysis (PCA)", Computers and Geosciences, 19, pp. 303{342 (1993).
34. Shi, L.C., Jiao, Y.Y., and Lu, B.L. \Differential entropy feature for EEG-based vigilance estimation", In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, IEEE, pp. 6627{6630 (2013).
35. Kirk, B.P. and LaCourse, J.R. \Vigilance monitoring from the eeg power spectrum with a neural network", In: Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, 3, IEEE, pp. 1218{1219 (1997).
36. Lin, C.T., Chuang, C.H., Huang, C.S., et al. \Wireless and wearable EEG system for evaluating driver vigilance", IEEE Transactions on Biomedical Circuits and Systems, 8(2), pp. 165{176 (2014).
37. Armanfard, N., Komeili, M., Reilly, J.P., et al. \Vigilance lapse identi cation using sparse EEG electrode arrays", In: Electrical and Computer Engineering (CCECE), 2016 IEEE Canadian Conference on IEEE, pp. 1{4 (2016).
38. Peng, H., Long, F., and Ding, C. \Feature selection based on mutual information criteria of maxdependency,max-relevance, and min-redundancy", IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), pp. 1226{1238 (2005).
39. Cao, L., Li, J., Sun, Y., et al. \Eeg-based vigilance analysis by using fisher score and pca algorithm", In: Progress in Informatics and Computing (PIC), 1, IEEE, pp. 175{179 (2010).
40. Guo, Z., Pan, Y., Zhao, G., et al. \Detection of driver vigilance level using eeg signals and driving contexts", IEEE Transactions on Reliability, 67(1), pp. 370{380(2018).
41. Chen, Y., Farrand, J., Tang, J., et al. \Relationship between amplitude of resting-state fNIRS global signal and EEG vigilance measures", In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, IEEE, pp. 537{540 (2017).
42. Cynthia, A., Patricia, G., Nisrine, J., et al. \A new system for detecting fatigue and sleepiness using brain connectivity: EEg based estimation of fatigue, vigilance and sleepiness for drivers", In: Advances in Biomedical Engineering (ICABME), 2017 Fourth International Conference on IEEE, pp. 1{4 (2017).
43. Baccala, L.A., Sameshima, K., and Takahashi, D.\Generalized partial directed coherence", In: Digital Signal Processing, 2007 15th International Conference on. IEEE, pp. 163{166 (2007).
44. Pudil, P., Novovicova, J., and Kittler, J. \Floating search methods in feature selection", Pattern Recognition Letters, 15(11), pp. 1119{1125 (1994).
45. Li, M., Fu, J.W., and Lu, B.L. \Estimating vigilance in driving simulation using probabilistic PCA", In: Engineering in Medicine and Biology Society, 2008. EMBS 2008, 30th Annual International Conference of the IEEE, IEEE, pp. 5000{5003 (2008).
46. Tipping, M.E. and Bishop, C.M. \Probabilistic principal component analysis", Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3), pp. 611{622 (1999).
47. Mohammadpour, M. and Mozaffari, S. \Classification of EEG-based attention for brain computer interface", In: Intelligent Systems and Signal Processing (ICSPIS), 2017 3rd Iranian Conference on. IEEE, pp. 34{37 (2017).
48. Ning, T. and Bronzino, J.D. \Bispectral analysis of the rat eeg during various vigilance states", IEEE Transactions on Biomedical Engineering, 36(4), pp. 497{499 (1989).
49. Ouyang, T. and Lu, H.T. \Vigilance analysis based on continuous wavelet transform of eeg signals", In: Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on. IEEE, pp. 1{4 (2010).
50. Yu, H., Lu, H., Ouyang, T., et al. \Vigilance detection based on sparse representation of eeg", In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, IEEE pp. 2439{2442 (2010).
51. Wright, J., Yang, A.Y., Ganesh, A., et al. \Robust face recognition via sparse representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), pp. 210{227 (2009).
52. Friedman, J.H. and Stuetzle, W. \Projection pursuit regression", Journal of the American Statistical Association, 76(376), pp. 817{823 (1981).
53. Lago, P., Rocha, A., and Jones, N. \Covariance density estimation for autoregressive spectral modelling of point processes", Biological  Cybernetics, 61(3), pp. 195{203 (1989).
54. Welch, P. \The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms", IEEE Transactions on Audio and Electroacoustics, 15(2), pp. 70{73 (1967).
55. Shen, K.Q., Ong, C.J., Li, X.P., et al. \A feature selection method for multilevel mental fatigue EEG classification", IEEE Transactions on Biomedical Engineering, 54(7), pp. 1231{1237 (2007).
56. Plante, D.T., Goldstein, M.R., Cook, J.D., et al. \Effects of partial sleep deprivation on slow waves during non-rapid eye movement sleep: a high density EEG investigation", Clinical Neurophysiology, 127(2), pp. 1436{1444 (2016).
57. Neu, D., Mairesse, O., Verbanck, P., et al. \Nonrem sleep EEG power distribution in fatigue and sleepiness", Journal of Psychosomatic Research, 76(4), pp. 286{291 (2014).
58. Durocher, M., Chebana, F., and Ouarda, T.B. \A nonlinear approach to regional of hydrometeorology", 16(4), pp. 1561{1574 (2015).
Scientia Iranica
Volume 29, Issue 3
Transactions on Computer Science & Engineering and Electrical Engineering (D)
May and June 2022
Pages 1486-1505

Files

History

  • Receive Date: 30 June 2019
  • Revise Date: 26 November 2019
  • Accept Date: 15 June 2020

Share

How to cite

Statistics