航天医学与医学工程

参考文献

[1]Babiloni F.Mental workload monitoring:new perspectives from neuroscience[C]//International Symposium on Human Mental Workload:Models and Applications.Rome,Italy:Springer,Cham,2019:3-19.

[2]Puma S,Matton N,Paubel PV,et al.Using theta and alpha band power to assess cognitive workload in multitasking environments[J].International Journal of Psychophysiology,2018,123:111-120.

[3]Chin ZY,Zhang X,Wang C,et al.EEG-based discrimination of different cognitive workload levels from mental arithmetic[C]//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC).Hawaii,USA:IEEE,2018:1984-1987.

[4]Baig MZ,Kavakli M.Analyzing novice and expert user’s cognitive load in using multi-modal interface system[C]//2018 26th International Conference on Systems Engineering(ICSEng).Sydney,Australia:IEEE,2018:1-7.

[5]Kramer AF,Sirevaag EJ,Braune R.psychophysiological assessment of operator workload during simulated flight missions[J].Human factors,1987,29(2):145-160.

[6]明东,柯余峰,何峰,等.基于生理信号的脑力负荷检测及自适应自动化系统研究:40年回顾与最新进展[J].电子测量与仪器学报,2015,29(1):1-13.Ming D,Ke YF,He F,et al.Psychophysiological measures based studies on mental workload assessment and adaptive automation:Review of the last 40 years and the latest developments[J].Journal of Electronic Measurement and Instrument,2015,29(1):1-13

[7]Heard J,Harriott CE,Adams JA.survey of workload assessment algorithms[J].IEEE Transactions on HumanMachine Systems,2018,48(5):434-451.

[8]Charles RL,Nixon J.Measuring mental workload using physiological measures:systematic review[J].Applied Ergonomics,2019,74:221-232.

[9]Aghajani H,Garbey M,Omurtag A.Measuring mental workload with EEG+f NIRS[J].Frontiers in Human Neuroscience,2017,11:359.

[10]Pergher V,Wittevrongel B,Tournoy J,et al.Mental workload of young and older adults gauged with ERPs and spectral power during N-Back task performance[J].Biological Psychology,2019,146(9):107726.

[11]Zhang Y,Shen Y.Parallel mechanism of spectral featureenhanced maps in EEG-based cognitive workload classification[J].Sensors,2019,19(4):808.

[12]Zhang D,Zhao H,Bai W,et al.Functional connectivity among multi-channel EEGs when working memory load reaches the capacity[J].Brain Research,2016,1631:101-112.

[13]So WKY,Wong SWH,Mak JN,et al.An evaluation of mental workload with frontal EEG[J].Plo SOne,2017,12(4):e0174949.

[14]Radüntz T.new method for the objective registration of mental workload[C]//Advances in Neuroergonomics and Cognitive Engineering.Florida,USA:Springer,Cham,2017:279-290.

[15]Blanco JA,Johnson MK,Jaquess KJ,et al.Quantifying cognitive workload in simulated flight using passive,dry EEG measurements[J].IEEE Transactions on Cognitive and Developmental Systems,2016,10(2):373-383.

[16]Kim NY,House R,Yun MH,et al.Neural correlates of workload transition in Multitasking:An ACT-R model of hysteresis effect[J].Frontiers in Human Neuroscience,2019,12:535.

[17]Zhang J,Cui X,Li J,et al.Imbalanced classification of mental workload using cost-sensitive majority weighted minority oversampling strategy[J].Cognition,Technology Work,2017,19(4):633-653.

[18]Almogbel MA,Dang AH,Kameyama W.Cognitive workload detection from raw EEG-signals of vehicle driver using deep learning[C]//2019 21st International Conference on Advanced Communication Technology(ICACT).PyeongChang,Korea:IEEE,2019:1-6.

[19]Delorme A,Makeig S.EEGLAB:An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J].Journal of Neuroscience Methods,2004,134(1):9-21.

[20]Causse M,Fabre E,Giraudet L,et al.EEG/ERP as measure of mental workload in simple piloting task[J].Procedia Manufacturing,2015,3:5230-5236.

[21]Dehais F,Duprès A,Blum S,et al.Monitoring pilot’s mental workload using ERPs and spectral power with six-dry-electrode EEG system in real flight conditions[J].Sensors,2019,19(6):1324.

[22]Wang S,Gwizdka J,Chaovalitwongse WA.Using wireless EEG signals to assess memory workload in the n-back task[J].IEEE Transactions on Human-Machine Systems,2015,46(3):424-435.

[23]Wang F,Wu S,Zhang W,et al.Multiple nonlinear features fusion based driving fatigue detection[J].Biomedical Signal Processing and Control,2020,62:102075.

[24]Chaudhuri A,Routray A.Driver fatigue detection through chaotic entropy analysis of cortical sources obtained from scalp EEG signals[J].IEEE Transactions on Intelligent Transportation Systems,2019,21(1):185-198.

[25]Yang S,Yin Z,Wang Y,et al.Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders[J].Computers in Biology and Medicine,2019,109:159-170.

[26]Cheema BS,Samima S,Sarma M,et al.Mental workload estimation from EEG signals using machine learning algorithms[C]//International Conference on Engineering Psychology and Cognitive Ergonomics.Las Vegas,USA:Springer,Cham,2018:265-284.

[27]Bashivan P,Yeasin M,Bidelman GM.Single trial prediction of normal and excessive cognitive load through EEGfeature fusion[C]//2015 IEEE Signal Processing in Medicine and Biology Symposium(SPMB).Philadelphia,USA:IEEE,2015:1-5.

[28]Calcagno A,Coelli S,Couceiro R,et al.EEG monitoring during software development[C]//2020 IEEE 20th Mediterranean Electrotechnical Conference(MELECON).Palermo,Italy:IEEE,2020:325-329.

[29]Gudmundsson S,Runarsson TP,Sigurdsson S,et al.Reliability of quantitative EEG features[J].Clinical Neurophysiology,2007,118(10):2162-2171.

[30]Rojas RF,Debie E,Fidock J,et al.Electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding swarm of unmanned ground vehicles in contested environments[J].Frontiers in Neuroscience,2020,1:1-10.

[31]Christensen JC,Estepp JR,Wilson GF,et al.The effects of day-to-day variability of physiological data on operator functional state classification[J].Neuro Image,2012,59(1):57-63.

[32]Plechawska-Wójcik M,Tokovarov M,Kaczorowska M,et al.three-class classification of cognitive workload based on EEG spectral data[J].Applied Sciences,2019,9(24):5340.

[33]Gao Z,Gong Z,Cai Q,et al.Complex Network Analysis of Experimental EEG Signals for Decoding Brain Cognitive State[J].IEEE Transactions on Circuits and Systems II:Express Briefs,2020,68(1):531-535.

[34]Dimitrakopoulos GN,Kakkos I,Dai Z,et al.Task-independent mental workload classification based upon common multiband EEG cortical connectivity[J].IEEETransactions on Neural Systems and Rehabilitation Engineering,2017,25(11):1940-1949.

[35]Kakkos I,Dimitrakopoulos GN,Gao L,et al.Mental workload drives different reorganizations of functional cortical connectivity between 2D and 3D simulated flight experiments[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2019,27(9):1704-1713.

[36]He B,Astolfi L,Valdés-Sosa PA,et al.Electrophysiological brain connectivity:theory and implementation[J].IEEE Transactions on Biomedical Engineering,2019,66(7):2115-2137.

[37]Arvaneh M,Umilta A,Robertson IH.Filter bank common spatial patterns in mental workload estimation[C]//2015 37th Annual International Conference of the IEEEEngineering in Medicine and Biology Society(EMBC).Milan,Italy:IEEE,2015:4749-4752.

[38]Fu R,Han M,Tian Y,et al.Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis[J].Journal of Neuroscience Methods,2020,343:108833.

[39]Zhang P,Wang X,Chen J,et al.Spectral and temporal feature learning with two-stream neural networks for mental workload assessment[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2019,27(6):1149-1159.

[40]Cai J,Luo J,Wang S,et al.Feature selection in machine learning:new perspective[J].Neurocomputing,2018,300:70-79.

[41]Vergara JR,Estévez PA.review of feature selection methods based on mutual information[J].Neural Computing and Applications,2014,24(1):175-186.

[42]Kosti MV,Georgiadis K,Adamos DA,et al.Towards an affordable brain computer interface for the assessment of programmers’mental workload[J].International Journal of Human-Computer Studies,2018,115:52-66.

[43]Arico P,Borghini G,Di Flumeri G,et al.Reliability over time of EEG-based mental workload evaluation during Air Traffic Management(ATM) tasks[C]//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC).Milan,Italy:IEEE,2015:7242-7245.

[44]AricòP,Borghini G,Di Flumeri G,et al.Adaptive automation triggered by EEG-based mental workload index:passive brain-computer interface application in realistic air traffic control environment[J].Frontiers in Human Neuroscience,2016,10:539.

[45]Sciaraffa N,AricòP,Borghini G,et al.On the use of machine learning for EEG-based workload assessment:algorithms comparison in realistic task[C]//International Symposium on Human Mental Workload:Models and Applications.Rome,Italy:Springer,Cham,2019:170-185.

[46]Hefron RG,Borghetti BJ,Christensen JC,et al.Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation[J].Pattern Recognition Letters,2017,94:96-104.

[47]Zhang L,Wade J,Bian D,et al.Cognitive load measurement in virtual reality-based driving system for autism intervention[J].IEEE Transactions on Affective Computing,2017,8(2):176-189.

[48]Yin Z,Zhang J.Cross-session classification of mental workload levels using EEG and an adaptive deep learning model[J].Biomedical Signal Processing and Control,2017,33:30-47.

[49]Di Flumeri G,Borghini G,AricòP,et al.EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings[J].Frontiers in Human Neuroscience,2018,12:509

[50]Kim HS,Yoon DS,Lee SJ,et al.study on the cognitive workload characteristics according to the driving behavior in the urban road[C]//2018 International Conference on Electronics,Information,and Communication(ICEIC).Honolulu,USA:IEEE,2018:1-4.

[51]Dimitrakopoulos GN,Kakkos I,Dai Z,et al.Functional connectivityanalysisofmentalfatiguerevealsdifferent network topological alterations between driving and vigilance tasks[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2018,26(4):740-749.

[52]Plazak J,Di Govannii DA,Collins DL,et al.Cognitive load associations when utilizing auditory display within image-guided neurosurgery[J].International Journal of Computer Assisted Radiology and Surgery,2019,14(8):1431-1438.

[53]Morales JM,Ruiz-Rabelo JF,Diaz-Piedra C,et al.Detecting mental workload in surgical teams using wearable single-channel electroencephalographic device[J].Journal of Surgical Education,2019,76(4):1107-1115.

[54]Richer N,Downey RJ,Hairston WD,et al.Motion and muscle artifact removal validation using an electrical head phantom,robotic motion platform,and dual layer mobile EEG[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2020,28(8):1825-1835.

[55]Zammouri A,Moussa AA,Mebrouk Y.Brain-computer interface for workload estimation:Assessment of mental efforts in learning processes[J].Expert Systems with Applications,2018,112:138-147.

[56]Baldwin CL,Penaranda BN.Adaptive training using an artificial neural network and EEG metrics for within-and cross-task workload classification[J].Neuro Image,2012,59(1):48-56.

[57]Zhang P,Wang X,Zhang W,et al.Learning spatialspectral-temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2018,27(1):31-42.

[58]Debie E,Rojas RF,Fidock J,et al.Multimodal fusion for objective assessment of cognitive workload:review[J].IEEE Transactions on Cybernetics,2019:51(3):1542-1555.

[59]Craik A,He Y,Contreras-Vidal JL.Deep learning for electroencephalogram(EEG) classification tasks:review[J].Journal of Neural Engineering,2019,16(3):031001.