[1] [1] Hoc J M. From human-machine interaction to human-machine cooperation[J]. Ergonomics, 2000, 43(7): 833-843.

[2] [2] Zhang J H, Peng X D, Hua L, et al.. Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method[J]. Cognitive Neurodynamics, 2013, 7(6): 477-494.

[3] [3] Parasuraman R, Cosenzo K A, Visser E D. Adaptive automation for human supervision of multiple uninhabited vehicles: Effects on change detection, situation awareness and mental workload[J]. Journal of Environmental Science & Health Part A, 2010, 38(2): 289-299.

[4] [4] Wilson G F, Russell C A, Davis I. The importance of determining individual operator capabilities when applying adaptive aiding[C]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2006: 141-145.

[5] [5] Borghini G, Astolfi L, Vecchiato G, et al.. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness [J]. Neuroscience & Biobehavioral Reviews, 2014, 44: 58-75.

[6] [6] Qin G, Yang W, Fei S, et al.. Mental workload measurement for emergency operating procedures in digital nuclear power plants [J]. Ergonomics, 2013, 56(7): 1070-1085.

[7] [7] Wang Raofen. Fuzzy modeling method′s research of operator functional state[D]. Shanghai: East China University of Science and Technology,2012: 3-5.

[8] [8] Yang S, Zhang J. An adaptive human-machine control system based on multiple fuzzy predictive models of operator functional state[J]. Biomedical Signal Processing & Control, 2013, 8(3): 302-310.

[9] [9] Fallahi M, Motamedzade M, Heidarimoghadam R, et al.. Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study[J]. Applied Ergonomics, 2016, 52: 95-103.

[10] [10] Kopf J, Dresler T, Reicherts P, et al.. Effect of emotional content on brain activation and the late positive potential in a word n-back task[J]. PLoS ONE, 2013, 8(9): e75598.

[11] [11] Townsend J D, Sugar C A, Walshaw P D, et al.. Front striatal neuroimaging findings differ in patients with bipolar disorder who have or do not have ADHD comorbidity[J]. Journal of Affective Disorders, 2013, 147(1-3): 389-396.

[12] [12] Yang Shaozeng. Prediction and regulation of operator functional state based on multiple physiological data and fuzzy modeling methods[D]. Shanghai: East China University of Science and Technology, 2014: 7-16.

[13] [13] Ayaz H, Shewokis P A, Bunce S, et al.. Optical brain monitoring for operator training and mental workload assessment [J]. NeuroImage, 2012, 59(1): 36-47.

[14] [14] Pan Jinjin, Jiao Xuejun, Jiang Jin, et al.. Mental workload assessment based on functional near-infrared spectroscopy[J]. Acta Optica Sinica, 2014, 34(11): 1130002.

[15] [15] Pan Jinjin, Jiao Xuejun, Jiao Dian, et al.. Study on variation in cortex oxygen with task features using functional near-infrared spectroscopy[J]. Acta Optica Sinica, 2015, 35(8): 0817001.

[16] [16] Izzetoglu K, Ayaz H, Hing J T, et al.. UAV operators workload assessment by optical brain imaging technology (fNIR)[M]. //Handbook of unmanned aerial vehicles. New York: Springer, 2015: 2475-2500

[17] [17] Zhang Yong, Chen Bin, Li Dong. A three-dimensional geometric Monte Carlo method for simulation of light propagation in biological tissues[J]. Chinese J Lasers, 2015, 42(1): 0104003.

[18] [18] Xiong Yang, Si Minzhen, Gao Fei, et al.. Study on cervical cancer oxyhemoglobin using near-infrared surface-enhanced Raman spectroscopy[J]. Chinese J Lasers, 2015, 42(1): 0115001.

[19] [19] Chiarelli A M, Maclin E L, Fabiani M, et al.. A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data[J]. NeuroImage, 2015, 112: 128-137.

[20] [20] Wu Chunyang, Lu Qipeng, Ding Haiquan, et al.. Near-infrared non-invasive blood glucose measurement using human tissue fluid[J]. Acta Optica Sinica, 2013, 33(11): 1117001.

[21] [21] Zhou Zhenyu, Yang Hongyu, Gong Hui, et al.. Brain signal analysis of functional near-infrared imaging based on Hilbert-Huang transform[J]. Acta Optica Sinica, 2007, 27(2): 307-312.

[22] [22] Piper S K, Krueger A, Koch S P, et al.. A wearable multi-channel fNIRS system for brain imaging in freely moving subjects [J]. NeuroImage, 2014, 85(2): 64-67.

[23] [23] Hall M, Chaudhary U, Rey G, et al.. Fronto-temporal mapping and connectivity using NIRS for language-related paradigms[J]. Journal of Neurolinguistics, 2013, 26(1): 178-194.

[24] [24] Doi H, Nishitani S, Shinohara K. NIRS as a tool for assaying emotional function in the prefrontal cortex[J]. Frontiers in Human Neuroscience, 2013, 7(1): 56-67.

[25] [25] Ozawa S, Matsuda G, Hiraki K. Negative emotion modulates prefrontal cortex activity during a working memory task: A NIRS study[J]. Frontiers in Human Neuroscience, 2014, 8(2): 46.

[26] [26] Fishburn F A, Norr M E, Medvedev A V, et al.. Sensitivity of fNIRS to cognitive state and load[J]. Frontiers in Human Neuroscience, 2014, 8(2): 76.

[27] [27] Lobacheva E M, Galatenko Y N, Gabidullina R F, et al.. Automated real-time classification of functional states based on physiological parameters[J]. Procedia-Social and Behavioral Sciences, 2013, 86: 373-378.

[28] [28] Wilson G F, Russell C A. Real-time assessment of mental workload using psychophysiological measures and artificial neural networks[J]. Journal of the Human Factors & Ergonomics Society, 2003, 45(4): 635-643.

[29] [29] Zhong Y, Zhang J. Operator functional state classification using least-square support vector machine based on recursive feature elimination technique[J]. Computer Methods & Programs in Biomedicine, 2014, 113(1): 101-115.

[30] [30] Ting C H, Mahfouf M, Nassef A, et al.. Real-time adaptive automation system based on identification of operator functional state in simulated process control operations[J]. IEEE Transactions on Systems Man & Cybernetics Part A, 2010, 40(2): 251-262.

[31] [31] Kirilina E, Jelzow A, Heine A, et al.. The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy[J]. NeuroImage, 2012, 61(1): 70-81.

[32] [32] Xu C, Bray S, Reiss A L. Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics[J]. NeuroImage, 2010, 49(4): 3039-3046.

[33] [33] Haeussinger F B, Sler T, Heinzel S, et al.. Reconstructing functional near-infrared spectroscopy (fNIRS) signals impaired by extra-cranial confounds: An easy-to-use filter method[J]. NeuroImage, 2014, 95(8): 69-79.

[34] [34] Peng Mingjin, Li Zhi. Analysis and feature extraction of laser micro-Doppler signatures based on Hilbert-Huang transforms [J]. Chinese J Lasers, 2013, 40(8): 0809004.

[35] [35] Xu Lu. A study on feature selection algorithm based on SVM-RFE and particle swarm optimization[D]. Changsha: Hunan Normal University, 2014: 63-75.

[36] [36] Durantin G, Gagnon J F, Tremblay S, et al.. Using near infrared spectroscopy and heart rate variability to detect mental overload[J]. Behavioural Brain Research, 2014, 259(2): 16-23.

[37] [37] Hu T Y, Xie X, Li J. Negative or positive The effect of emotion and mood on risky driving[J]. Transportation Research Part F, 2013, 16: 29-40.