Fatigue driving recognition based on deep learning and graph neural network

[1]  Rongrong Fu,et al.  Dynamic driver fatigue detection using hidden Markov model in real driving condition , 2016, Expert Syst. Appl..

[2]  Taorong Qiu,et al.  The functional brain network based on the combination of shortest path tree and its application in fatigue driving state recognition and analysis of the neural mechanism of fatigue driving , 2020, Biomed. Signal Process. Control..

[3]  Nitish Thakor,et al.  Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Fabio Babiloni,et al.  Assessment of driving fatigue based on intra/inter-region phase synchronization , 2017, Neurocomputing.

[5]  Wenming Zheng,et al.  EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks , 2020, IEEE Transactions on Affective Computing.

[6]  Hongtao Wang,et al.  Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation , 2020, IEEE Transactions on Cognitive and Developmental Systems.

[7]  Chao Liu,et al.  Research on fatigue driving detection using forehead EEG based on adaptive multi-scale entropy , 2019, Biomed. Signal Process. Control..

[8]  June-Goo Lee,et al.  Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.

[9]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Erwan Scornet,et al.  A random forest guided tour , 2015, TEST.

[11]  Temple F. Smith Occam's razor , 1980, Nature.

[12]  Yu Sun,et al.  Dynamic Reorganization of Functional Connectivity Unmasks Fatigue Related Performance Declines in Simulated Driving , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  Evangelos Bekiaris,et al.  Using EEG spectral components to assess algorithms for detecting fatigue , 2009, Expert Syst. Appl..

[14]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[15]  Kemal Polat,et al.  Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform , 2007, Appl. Math. Comput..

[16]  W. Zuo,et al.  Deep Learning on Image Denoising: An overview , 2019, Neural Networks.

[17]  Taorong Qiu,et al.  Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis , 2018, Entropy.

[18]  William Stafford Noble,et al.  Support vector machine , 2013 .

[19]  M. J. Katz,et al.  Fractals and the analysis of waveforms. , 1988, Computers in biology and medicine.

[20]  Nikhil R. Pal,et al.  Feature selection with SVD entropy: Some modification and extension , 2014, Inf. Sci..

[21]  Onur Avci,et al.  1-D Convolutional Neural Networks for Signal Processing Applications , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Sergey Ioffe,et al.  Probabilistic Linear Discriminant Analysis , 2006, ECCV.

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Jacques Bergeron,et al.  Monotony of road environment and driver fatigue: a simulator study. , 2003, Accident; analysis and prevention.

[25]  Hung Hung,et al.  Matrix variate logistic regression model with application to EEG data. , 2011, Biostatistics.

[26]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[27]  Bhavna P Harne,et al.  Higuchi Fractal Dimension Analysis of EEG Signal before and after OM Chanting to Observe Overall Effect on Brain , 2014 .

[28]  Xinmin Wang,et al.  EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Zhendong Mu,et al.  Driving Fatigue Detecting Based on EEG Signals of Forehead Area , 2017, Int. J. Pattern Recognit. Artif. Intell..

[30]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[31]  Arthur Petrosian,et al.  Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns , 1995, Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems.

[32]  Wei-Dong Dang,et al.  Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection , 2019, Int. J. Neural Syst..

[33]  Karl J. Friston,et al.  Structural and Functional Brain Networks: From Connections to Cognition , 2013, Science.

[34]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .