Alioua N, Amine A, Rziza M (2014) Driver’s fatigue detection based on yawning extraction. Int J Veh Technol 2014:47–75

Google Scholar 

Bergasa LM, Nuevo J, Sotelo MA, Vhzquez M (2006) Real-time system for monitoring driver vigilance. IEEE Trans Intell Transp Syst 7:63–77

Article  Google Scholar 

Buendia R, Forcolin F, Karlsson J, Sjöqvist BA, Anund A, Candefjord S (2019) Deriving heart rate variability indices from cardiac monitoring-an indicator of driver sleepiness. Traffic Inj Prev 20:249–254

Article  PubMed  Google Scholar 

Cao Z, Chuang C-H, King J-K, Lin C-T (2019) Multi-channel eeg recordings during a sustained-attention driving task. Sci Data

Chao H, Dong L, Liu Y, Lu B (2019) Emotion recognition from multiband eeg signals using capsnet. Sensors (Switzerland) 19

Chen S, Kaili X, Yao X, Ge J, Li L, Zhu S, Li Z (2021) Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals. Comput Methods Programs Biomed 211:106451

Article  PubMed  Google Scholar 

Chuang CH, Ko LW, Lin YP, Jung TP, Lin CT (2014) Independent component ensemble of eeg for brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 22:230–238

Article  PubMed  Google Scholar 

Dong Y, Hu Z, Uchimura K, Murayama N (2011) Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans Intell Transp Syst 12:596–614

Article  Google Scholar 

Flores MJ, Armingol JM, Adl Escalera (2010) Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions. In: Ad Hoc networks

Gao Z, Wang X, Yang Y, Chaoxu M, Cai Q, Dang W, Zuo S (2019) Eeg-based spatio-temporal convolutional neural network for driver fatigue evaluation. IEEE Trans Neural Netw Learn Syst 30:2755–2763

Article  PubMed  Google Scholar 

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Neural information processing systems

Guarda L, Tapia J, Droguett EL, Ramos M (2022) A novel capsule neural network based model for drowsiness detection using electroencephalography signals. Expert Syst Appl 201

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778

Jianchao L, Zheng X, Tang L, Zhang T, Sheng QZ, Wang C, Jin J, Shui Yu, Zhou W (2021) Can steering wheel detect your driving fatigue? IEEE Trans Veh Technol 70:5537–5550

Article  Google Scholar 

Jiang Y, Zhang Y, Lin C, Dongrui W, Lin C-T (2021) Eeg-based driver drowsiness estimation using an online multi-view and transfer tsk fuzzy system. IEEE Trans Intell Transp Syst 22(3):1752–1764

Article  Google Scholar 

Jiao Y, Deng Y, Luo Y, Lu BL (2020) Driver sleepiness detection from eeg and eog signals using gan and lstm networks. Neurocomputing 408:100–111

Article  Google Scholar 

Kamakura Y, Ohsuga M, Inoue Y, Noguchi Y (2007) Classification of blink waveforms towards the assessment of driver’s arousal level. Trans Soc Autom Engineers Jpn 38:173–178

Google Scholar 

Kingma D, Ba J (2014) Adam: a method for stochastic optimization. Comput Sci

Ko LW, Komarov O, Lai WK, Liang WG, Jung TP (2020) Eyeblink recognition improves fatigue prediction from single-channel forehead eeg in a realistic sustained attention task. J Neural Eng 17:036015 ((12pp))

Article  PubMed  Google Scholar 

Kong W, Zhou Z, Jiang B, Babiloni F, Borghini G (2017) Assessment of driving fatigue based on intra/inter-region phase synchronization. Neurocomputing 219:474–482

Article  Google Scholar 

Ko W, Oh K, Jeon E, Suk H-I (2020) Vignet: a deep convolutional neural network for eeg-based driver vigilance estimation. In: 2020 8th International Winter Conference on Brain-Computer Interface (BCI), pp 1–3

Liu Y, Lan Z, Cui J, Sourina O, Muller-Wittig W (2019) Eeg-based cross-subject mental fatigue recognition. In: Proceedings—2019 international conference on cyberworlds, CW 2019, pp 247–252

Lu BL, Li H, Zheng WL (2018) Multimodal vigilance estimation with adversarial domain adaptation networks. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp 1–6

Luo H, Qiu T, Liu C, Huang P (2019) Research on fatigue driving detection using forehead eeg based on adaptive multi-scale entropy. Biomed Signal Process Control 51:50–58

Article  Google Scholar 

Maaten L, Geoffrey H (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579–2605

Google Scholar 

Ma B, Li H, Luo Y, Lu BL (2019) Depersonalized cross-subject vigilance estimation with adversarial domain generalization. In: 2019 International joint conference on neural networks (IJCNN), pp 1–8

Maskeliunas R, Damasevicius R, Martisius I, Vasiljevas M (2016) Consumer-grade eeg devices: Are they usable for control tasks? PeerJ

Murphy-Chutorian E, Trivedi MM (2010) Head pose estimation and augmented reality tracking: an integrated system and evaluation for monitoring driver awareness. IEEE Trans Intell Transp Syst 11:300–311

Article  Google Scholar 

Naeije M, Zorn H (1982) Relation between emg power spectrum shifts and muscle fibre action potential conduction velocity changes during local muscular fatigue in man. Eur J Appl Physiol 50:23–33

Article  Google Scholar 

Pei Z, Wang H, Bezerianos A, Li J (2021) Eeg-based multiclass workload identification using feature fusion and selection. IEEE Trans Instru Measure 70:4001108

Article  Google Scholar 

Ratti E, Waninger S, Berka C, Ruffini G, Verma A (2017) Comparison of medical and consumer wireless eeg systems for use in clinical trials. Front Human Neurosci 11

Rogado E, García JL, Barea R, Bergasa LM, López E (2009) Driver fatigue detection system. In: 2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008, pp 1105–1110

Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Proc Adv Neural Inf Process Syst 3856–3866

Sikander G, Anwar S (2019) Driver fatigue detection systems: a review. IEEE Trans Intell Transp Syst 20:2339–2352

Article  Google Scholar 

Trutschel U, Sirois B, Sommer D, Golz M, Edwards D (2011) Perclos: an alertness measure of the past. In: Driving Assessment: International Driving Symposium on Human Factors in Driver Assessment

Tuncer T, Dogan S, Ertam F, Subasi A (2021) A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing eeg signals. Cogn Neurodyn 15:223–237

Article  PubMed  Google Scholar 

Vaswani A, Brain G, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need

Wei W, JonathanWu QM, Sun W, Yang Y, Yuan X, Zheng WL, Lu BL (2021) A regression method with subnetwork neurons for vigilance estimation using eog and eeg. IEEE Trans Cogn Develop Syst 13:209–222

Article  Google Scholar 

Wu W, Sun W, Jonathan Wu QM, Yang Y, Zhang H, Zheng WL, Lu BL (2022) Multimodal vigilance estimation using deep learning. IEEE Trans Cybern 52:3097–3110

Article  PubMed  Google Scholar 

Yue W, Ji Q (2019) Facial landmark detection: a literature survey. Int J Comput Vision 127:115–142

Article  Google Scholar 

Zeng H, Yang C, Dai G, Qin F, Zhang J, Kong W (2018) Eeg classification of driver mental states by deep learning. Cogn Neurodyn 12:597–606

Article  PubMed  PubMed Central  Google Scholar 

Zhang Y-F, Gao X-Y, Zhu J-Y, Zheng W-L, Lu B-L (2015) A novel approach to driving fatigue detection using forehead eog. In 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp 707–710

Zhang G, Etemad A (2021) Capsule attention for multimodal eeg-eog representation learning with application to driver vigilance estimation. IEEE Trans Neural Syst Rehabil Eng 29:1138–1149

Article  CAS  PubMed  Google Scholar 

Zhang C, Sun L, Cong F, Kujala T, Ristaniemi T, Parviainen T (2020) Optimal imaging of multi-channel eeg features based on a novel clustering technique for driver fatigue detection. Biomed Signal Process Control 62:102103

Article  Google Scholar 

Zhang Y, Guo R, Peng Y, Kong W, Nie F, Bao-Liang L (2022) An auto-weighting incremental random vector functional link network for eeg-based driving fatigue detection. IEEE Trans Instrum Meas 71:1–14

Google Scholar 

Zhang Y, Guo H, Zhou Y, Chengji X, Liao Y (2023) Recognising drivers’ mental fatigue based on eeg multi-dimensional feature selection and fusion. Biomed Signal Process Control 79:104237

Article  Google Scholar 

Zhao L, Li M, He Z, Ye S, Qin H, Zhu X, Dai Z (2022) Data-driven learning fatigue detection system: a multimodal fusion approach of ecg (electrocardiogram) and video signals. Measurement 201:111648

Article  Google Scholar 

Zheng WL, Lu BL (2017) A multimodal approach to estimating vigilance using eeg and forehead eog. J Neural Eng 14