References
- Nguyen, Thien. Utilization of a combined EEG/NIRS system to predict driver drowsiness. Scientific Reports. 10.1038/srep43933(2017)
- 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.
- MATOUSEK M, PETERSEN I. A method for assessing alertness fluctuations from EEG spectra [J]. Electroencephalogr Clin Neurophysiol, 1983, 55(1): 108–113.
- 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.
- 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.
- 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.
- 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.
- 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.
- Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, cybernetics. 1985:580–5.
- Barker AL. Selection of distance metrics and feature subsets for K-nearest neighbor classifiers: University of Virginia; 1997.
- Yang L, Jin R. Distance metric learning: A comprehensive survey. Michigan State Universiy. 2006; 2:4.
- 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.
- Oraii S. Eletrophysiology From Pants to Heart. USA: Books on Demand; 2012.
- Lakshmi MR, Prasad T, Prakash DVC. Survey on EEG signal processing methods. International Journal of Advanced Research in Computer Science Software Engineering. 2014; 4.
- Sanei S, Chambers J. EEG Signal Processing. England: John Wiley; 2007.
- 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.
- Lei X, Liao K. Understanding the influences of EEG reference: a large-scale brain network perspective. Frontiers in neuroscience. 2017; 11:205.
- 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.
- Nguyen, Thien. Utilization of a combined EEG/NIRS system to predict driver drowsiness. Scientific Reports. 10.1038/srep43933(2017)
- 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.
- MATOUSEK M, PETERSEN I. A method for assessing alertness fluctuations from EEG spectra [J]. Electroencephalogr Clin Neurophysiol, 1983, 55(1): 108–113.
- 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.
- 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.
- 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.
- 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.
- 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.
- Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, cybernetics. 1985:580–5.
- Barker AL. Selection of distance metrics and feature subsets for K-nearest neighbor classifiers: University of Virginia; 1997.
- Yang L, Jin R. Distance metric learning: A comprehensive survey. Michigan State Universiy. 2006; 2:4.
- 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.
- Oraii S. Eletrophysiology From Pants to Heart. USA: Books on Demand; 2012.
- Lakshmi MR, Prasad T, Prakash DVC. Survey on EEG signal processing methods. International Journal of Advanced Research in Computer Science Software Engineering. 2014; 4.
- Sanei S, Chambers J. EEG Signal Processing. England: John Wiley; 2007.
- 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.
- Lei X, Liao K. Understanding the influences of EEG reference: a large-scale brain network perspective. Frontiers in neuroscience. 2017; 11:205.
- 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.