1 |
MA J, ZHANG J, GONG Z Y, et al. Study on fatigue driving detection model based on steering operation features and eye movement features[C]//2018 IEEE 4th International Conference on Control Science and Systems Engineering. Wuhan, China: IEEE, 2018: 472-475.
|
2 |
SETIAWAN A, WIBAWA A D, PANE E S, et al. EEG-based mental fatigue detection using cognitive tests and RVM classification[C]//2019 International Conference of Artificial Intelligence and Information Technology(ICAIIT). Yogyakarta, Indonesia: IEEE, 2019: 180-185.
|
3 |
KARUPPUSAMY N S, KANG B Y. Multimodal system to detect driver fatigue using EEG, gyroscope, and image processing[J]. IEEE Access, 2020, 8: 129645-129667.
|
4 |
JAP B T, LAL S, FISCHER P, et al. Using EEG spectral components to assess algorithms for detecting fatigue[J]. Expert Systems With Applications, 2009, 36(2): 2352-2359.
|
5 |
SIEMIONOW V, FANG Y, CALABRESE L, et al. Altered central nervous system signal during motor performance in chronic fatigue syndrome[J]. Clinical Neurophysiology, 2004, 115(10): 2372-2381.
|
6 |
PAPADELIS C, KOURTIDOU-pAPADELI C, BAMIDIS P D, et al. Indicators of sleepiness in an ambulatory EEG study of night driving[C]//2006 International Conference of the IEEE Engineering in Medicine and Biology Society. New York, NY, USA: IEEE, 2006: 6201-6204.
|
7 |
WU Q, DENG P Y, QIU X Y, et al. Detecting fatigue status of pilots based on deep learning network using EEG signals[J]. IEEE Transactions on Cognitive and Developmental Systems, 2021, 13(3): 575-585.
|
8 |
MING Y R, WU D R, WANG Y K, et al. EEG-based drowsiness estimation for driving safety using deep Q-learning[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2021, 5(4): 583-594.
|
9 |
LIN C T, CHUANG C H, HUNG Y C, et al. A driving performance forecasting system based on brain dynamic state analysis using 4-D convolutional neural networks[J]. IEEE Transactions on Cybernetics, 2021, 51(10): 4959-4967.
|
10 |
DU G L, WANG Z Y, LI C Q, et al. A TSK-type convolutional recurrent fuzzy network for predicting driving fatigue[J]. IEEE Transactions on Fuzzy Systems, 2021, 29(8): 2100-2111.
|
11 |
ZHENG W L, LU B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks[J]. IEEE Transactions on Autonomous Mental Development, 2015, 7(3): 162-175.
|
12 |
LI J H, STRUZIK Z, ZHANG L Q, et al. Feature learning from incomplete EEG with denoising autoencoder[J]. Neurocomputing, 2015, 165: 23-31.
|
13 |
CECOTTI H, GRAESER A. Convolutional neural network with embedded fourier transform for EEG classification[C]//2008 19th International Conference on Pattern Recognition. Tampa, FL, USA: IEEE, 2008: 1-4.
|
14 |
ALHUSSEIN M, MUHAMMAD G, Hossain M S. EEG pathology detection based on deep learning[J]. IEEE Access, 2019, 7: 27781-27788.
|
15 |
SONG Y G, WANG D L, YUE K, et al. EEG-based motor imagery classification with deep multi-task learning[C]//2019 International Joint Conference on Neural Networks(IJCNN). Budapest, Hungary: IEEE, 2019: 1-8.
|
16 |
BHARDWAJ R, PARAMESWARAN S, BALASUBRAMANIAN V. Performance comparison of machine learning and deep learning while classifying driver's cognitive state[C]//2018 IEEE 13th International Conference on Industrial and Information Systems. Rupnagar, India: IEEE, 2018: 89-93.
|
17 |
孙曜, 文成林, 韦巍. 基于脑电和眼电的运动想象多尺度识别方法研究[J]. 电子学报, 2018, 46(3): 714-720.
|
|
SUN Y, WEN C L, WEI W. Research on EEG and EOG based multiscale recognization method of motor imagery[J]. Acta Electronica Sinica, 2018, 46(3): 714-720. (in Chinese)
|
18 |
张学军, 景鹏, 何涛, 等. 基于变分模态分解的癫痫脑电信号分类方法[J]. 电子学报, 2020, 48(12): 2469-2475.
|
|
ZHANG X J, JING P, HE T, et al. An epileptic electroencephalogram signal classification method based on variational mode decomposition[J]. Acta Electronica Sinica, 2020, 48(12): 2469-2475. (in Chinese)
|
19 |
AHMED S, MAURICIO MERINO L, MAO Z, et al. A deep learning method for classification of images RSVP events with EEG data[C]//2013 IEEE Global Conference on Signal and Information Processing. Austin, TX, USA: IEEE, 2013: 33-36.
|
20 |
GAO Y B, LEE H J, MEHMOOD R M. Deep learninig of EEG signals for emotion recognition[C]//2015 IEEE International Conference on Multimedia & Expo Workshops. Turin, Italy: IEEE, 2015: 1-5.
|
21 |
YANG Y X, GAO Z K, LI Y L, et al. A complex network-based broad learning system for detecting driver fatigue from EEG signals[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(9): 5800-5808.
|
22 |
ZHANG C, SUN L N, CONG F Y, et al. Spatiotemporal dynamical analysis of brain activity during mental fatigue process[J]. IEEE Transactions on Cognitive and Developmental Systems, 2021, 13(3): 593-606.
|
23 |
WU Q, ZHU L M, LI G J, et al. Nonparametric hierarchical hidden semi-Markov model for brain fatigue behavior detection of pilots during flight[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(6): 5245-5256.
|
24 |
CONG Y L, CHEN B, LIU H W, et al. Deep latent dirichlet allocation with topic-layer-adaptive stochastic gradient Riemannian MCMC[C]//ICML'17: Proceedings of the 34th International Conference on Machine Learning-Volume 70. Sydney, NSW, Australia: ACM. 2017: 864-873.
|
25 |
GOLUB G H, C F VAN LOAN. Matrix Computations[M]. Washington, D.C, USA: Johns Hopkins University Press, 2012.
|
26 |
WU X, SPALL J C. Improved Monte Carlo estimation of the fisher information matrix with independent perturbations[C]//2021 55th Annual Conference on Information Sciences and Systems(CISS). Baltimore, MD, USA: IEEE, 2021: 1-5.
|
27 |
SNYDER J P. Map Projections--A working Manual[M]. Washington, DC, USA: US Government Printing Office, 1987: 55-90.
|
28 |
BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks[C]//Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. Vancouver, B.C., Canada: MIT Press, 2007: 153-160.
|
29 |
AMARI S I. Natural gradient works efficiently in learning[J]. Neural Computation, 1998, 10(2): 251-276.
|
30 |
LAWHERN V J, SOLON A J, WAYTOWICH N R, et al. EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces[J]. Journal of Neural Engineering, 2018, 15(5): 056013.
|