Research on Fatigue Classification of Flight Simulation Training

[1] Nguyen, Thien. Utilization of a combined EEG/NIRS system to predict driver drowsiness. Scientific Reports. 10.1038/srep43933(2017) NguyenThien Utilization of a combined EEG/NIRS system to predict driver drowsiness Scientific Reports. 10.1038/srep43933(2017) Open DOISearch in Google Scholar

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[6] Q. Zeng, G. Li, Y. Cui, G. Jiang, and X. Pan, “Estimating temperature mortality exposure-response relationships and optimum ambient temperature at the multi-city level of china,” International journal of environmental research public health, vol. 13, no. 3, p. 279, 2016. ZengQ. LiG. CuiY. JiangG. PanX. “Estimating temperature mortality exposure-response relationships and optimum ambient temperature at the multi-city level of china,” International journal of environmental research public health 13 3 279 2016 Search in Google Scholar

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[8] Y. Du, P. Ma, X. Su, and Y. Zhang, “Driver fatigue detection based on eye state analysis,” in 11th Joint International Conference on Information Sciences. Atlantis Press, Conference Proceedings. DuY. MaP. SuX. ZhangY. “Driver fatigue detection based on eye state analysis,” in 11th Joint International Conference on Information Sciences Atlantis Press Conference Proceedings. Search in Google Scholar

[9] Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, cybernetics. 1985:580–5. KellerJM GrayMR GivensJA A fuzzy k-nearest neighbor algorithm IEEE transactions on systems, man, cybernetics 1985 580 5 Search in Google Scholar

[10] Barker AL. Selection of distance metrics and feature subsets for K-nearest neighbor classifiers: University of Virginia; 1997. BarkerAL Selection of distance metrics and feature subsets for K-nearest neighbor classifiers University of Virginia 1997 Search in Google Scholar

[11] Yang L, Jin R. Distance metric learning: A comprehensive survey. Michigan State Universiy. 2006; 2:4. YangL JinR Distance metric learning: A comprehensive survey Michigan State Universiy 2006 2 4 Search in Google Scholar

[12] Codella N, Cai J, Abedini M, Garnavi R, Halpern A, Smith JR. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. International workshop on machine learning in medical imaging: Springer; 2015. p. 118–26. CodellaN CaiJ AbediniM GarnaviR HalpernA SmithJR Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. International workshop on machine learning in medical imaging Springer 2015 118 26 Search in Google Scholar

[13] Oraii S. Eletrophysiology From Pants to Heart. USA: Books on Demand; 2012. OraiiS Eletrophysiology From Pants to Heart USA Books on Demand 2012 Search in Google Scholar

[14] Lakshmi MR, Prasad T, Prakash DVC. Survey on EEG signal processing methods. International Journal of Advanced Research in Computer Science Software Engineering. 2014; 4. LakshmiMR PrasadT PrakashDVC Survey on EEG signal processing methods International Journal of Advanced Research in Computer Science Software Engineering 2014 4 Search in Google Scholar

[15] Sanei S, Chambers J. EEG Signal Processing. England: John Wiley; 2007. SaneiS ChambersJ EEG Signal Processing England John Wiley 2007 Search in Google Scholar

[16] Sundararajan A, Pons A, Sarwat AI. A generic framework for eeg-based biometric authentication. 2015 12th International Conference on Information Technology-New Generations: IEEE; 2015. p. 139–44. SundararajanA PonsA SarwatAI A generic framework for eeg-based biometric authentication 2015 12th International Conference on Information Technology-New Generations IEEE 2015 139 44 Search in Google Scholar

[17] Lei X, Liao K. Understanding the influences of EEG reference: a large-scale brain network perspective. Frontiers in neuroscience. 2017; 11:205. LeiX LiaoK Understanding the influences of EEG reference: a large-scale brain network perspective Frontiers in neuroscience 2017 11 205 Search in Google Scholar

[18] Chella F, Pizzella V, Zappasodi F, Marzetti L. Impact of the reference choice on scalp EEG connectivity estimation. Journal of neural engineering. 2016; 13:036016. ChellaF PizzellaV ZappasodiF MarzettiL Impact of the reference choice on scalp EEG connectivity estimation Journal of neural engineering 2016 13 036016 Search in Google Scholar

[19] Nguyen, Thien. Utilization of a combined EEG/NIRS system to predict driver drowsiness. Scientific Reports. 10.1038/srep43933(2017) NguyenThien Utilization of a combined EEG/NIRS system to predict driver drowsiness Scientific Reports. 10.1038/srep43933(2017) Open DOISearch in Google Scholar

[20] ZETTERBERG L H. Estimation of parameters for a linear difference equaiton with application to EEG analysis [J]. Math Biosci, 1965, 5(3–4): 227–275. ZETTERBERGL H Estimation of parameters for a linear difference equaiton with application to EEG analysis [J] Math Biosci 1965 5 3–4 227 275 Search in Google Scholar

[21] MATOUSEK M, PETERSEN I. A method for assessing alertness fluctuations from EEG spectra [J]. Electroencephalogr Clin Neurophysiol, 1983, 55(1): 108–113. MATOUSEKM PETERSENI A method for assessing alertness fluctuations from EEG spectra [J] Electroencephalogr Clin Neurophysiol 1983 55 1 108 113 Search in Google Scholar

[22] MAKEIG S, JUNG T P. Changes in alertness are a principal component of variance in the EEG spectrum [J]. Neuroreport, 1995, 7(1): 213–216. MAKEIGS JUNGT P Changes in alertness are a principal component of variance in the EEG spectrum [J] Neuroreport 1995 7 1 213 216 Search in Google Scholar

[23] JAP B T, LAL S, FISCHER P, et al. Using EEG spectral components to assess algorithms for detecting fatigue[J]. Expert Syst Appl, 2009, 36(2): 2352–2359. JAPB T LALS FISCHERP Using EEG spectral components to assess algorithms for detecting fatigue[J] Expert Syst Appl 2009 36 2 2352 2359 Search in Google Scholar

[24] Q. Zeng, G. Li, Y. Cui, G. Jiang, and X. Pan, “Estimating temperaturemortality exposure-response relationships and optimum ambient temperature at the multi-city level of china,” International journal of environmental research public health, vol. 13, no. 3, p. 279, 2016. ZengQ. LiG. CuiY. JiangG. PanX. “Estimating temperaturemortality exposure-response relationships and optimum ambient temperature at the multi-city level of china,” International journal of environmental research public health 13 3 279 2016 Search in Google Scholar

[25] A. Baughman and E. A. Arens, “Indoor humidity and human health–part i: Literature review of health effects of humidity-influenced indoor pollutants,” ASHRAE Transactions, vol. 102, pp. 192–211, 1996. BaughmanA. ArensE. A. “Indoor humidity and human health–part i: Literature review of health effects of humidity-influenced indoor pollutants,” ASHRAE Transactions 102 192 211 1996 Search in Google Scholar

[26] Y. Du, P. Ma, X. Su, and Y. Zhang, “Driver fatigue detection based on eye state analysis,” in 11th Joint International Conference on Information Sciences. Atlantis Press, Conference Proceedings. DuY. MaP. SuX. ZhangY. “Driver fatigue detection based on eye state analysis,” in 11th Joint International Conference on Information Sciences Atlantis Press Conference Proceedings. Search in Google Scholar

[27] Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, cybernetics. 1985:580–5. KellerJM GrayMR GivensJA A fuzzy k-nearest neighbor algorithm IEEE transactions on systems, man, cybernetics 1985 580 5 Search in Google Scholar

[28] Barker AL. Selection of distance metrics and feature subsets for K-nearest neighbor classifiers: University of Virginia; 1997. BarkerAL Selection of distance metrics and feature subsets for K-nearest neighbor classifiers University of Virginia 1997 Search in Google Scholar

[29] Yang L, Jin R. Distance metric learning: A comprehensive survey. Michigan State Universiy. 2006; 2:4. YangL JinR Distance metric learning: A comprehensive survey Michigan State Universiy 2006 2 4 Search in Google Scholar

[30] Codella N, Cai J, Abedini M, Garnavi R, Halpern A, Smith JR. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. International workshop on machine learning in medical imaging: Springer; 2015. p. 118–26. CodellaN CaiJ AbediniM GarnaviR HalpernA SmithJR Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. International workshop on machine learning in medical imaging Springer 2015 118 26 Search in Google Scholar

[31] Oraii S. Eletrophysiology From Pants to Heart. USA: Books on Demand; 2012. OraiiS Eletrophysiology From Pants to Heart USA Books on Demand 2012 Search in Google Scholar

[32] Lakshmi MR, Prasad T, Prakash DVC. Survey on EEG signal processing methods. International Journal of Advanced Research in Computer Science Software Engineering. 2014; 4. LakshmiMR PrasadT PrakashDVC Survey on EEG signal processing methods International Journal of Advanced Research in Computer Science Software Engineering 2014 4 Search in Google Scholar

[33] Sanei S, Chambers J. EEG Signal Processing. England: John Wiley; 2007. SaneiS ChambersJ EEG Signal Processing England John Wiley 2007 Search in Google Scholar

[34] Sundararajan A, Pons A, Sarwat AI. A generic framework for eeg-based biometric authentication. 2015 12th International Conference on Information Technology-New Generations: IEEE; 2015. p. 139–44. SundararajanA PonsA SarwatAI A generic framework for eeg-based biometric authentication 2015 12th International Conference on Information Technology-New Generations IEEE 2015 139 44 Search in Google Scholar

[35] Lei X, Liao K. Understanding the influences of EEG reference: a large-scale brain network perspective. Frontiers in neuroscience. 2017; 11:205. LeiX LiaoK Understanding the influences of EEG reference: a large-scale brain network perspective Frontiers in neuroscience 2017 11 205 Search in Google Scholar

[36] Chella F, Pizzella V, Zappasodi F, Marzetti L. Impact of the reference choice on scalp EEG connectivity estimation. Journal of neural engineering. 2016; 13:036016. ChellaF PizzellaV ZappasodiF MarzettiL Impact of the reference choice on scalp EEG connectivity estimation Journal of neural engineering 2016 13 036016 Search in Google Scholar