Ahmadi A, Bazregarzadeh H, Kazemi K (2021) Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity. Biocybernet Biomed Eng 41(1):316–332

Article  Google Scholar 

Ahmad I, Wang X, Zhu M, Wang C, Pi Y, Khan JA, Khan S, Samuel OW, Chen S, Li G (2022) EEG-based epileptic seizure detection via machine/deep learning approaches: a systematic review. Comput Intell Neurosci 2022(1), 6486570

Altaheri H, Muhammad G, Alsulaiman M, Amin SU, Altuwaijri GA, Abdul W, Bencherif MA, Faisal M (2023) Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Comput Appl 35(20):14681–14722

Article  Google Scholar 

Barua S, Ahmed MU, Ahlström C, Begum S (2019) Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert Syst Appl 115:121–135

Article  Google Scholar 

Cai Q, Gao Z-K, Yang Y-X, Dang W-D, Grebogi C (2019) Multiplex limited penetrable horizontal visibility graph from EEG signals for driver fatigue detection. Int J Neural Syst 29(05):1850057

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 6:19. https://doi.org/10.1038/s41597-019-0027-4

Delorme A, Makeig S (2004) Eeglab: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

Article  PubMed  Google Scholar 

Dimitrakopoulos GN, Kakkos I, Dai Z, Wang H, Sgarbas K, Thakor N, Bezerianos A, Sun Y (2018) Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks. IEEE Trans Neural Syst Rehab Eng 26(4):740–749

Article  Google Scholar 

Gao Z, Dang W, Wang X, Hong X, Hou L, Ma K, Perc M (2021) Complex networks and deep learning for EEG signal analysis. Cogn Neurodyn 15:369–388

Article  PubMed  Google Scholar 

Gao D, Li P, Wang M, Liang Y, Liu S, Zhou J, Wang L, Zhang Y (2024) CSF-GTNet: A novel multi-dimensional feature fusion network based on Convnext-GeLU-BiLSTM for EEG-signals-enabled fatigue driving detection. IEEE J Biomed Health Informat 28(5):2558–2568

Geng Z, Chen Z, Meng Q, Han Y (2021) Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes. IEEE Trans Industr Inf 18(3):1521–1529

Article  Google Scholar 

Huang K-C, Chuang C-H, Wang Y-K, Hsieh C-Y, King J-T, Lin C-T (2019) The effects of different fatigue levels on brain-behavior relationships in driving. Brain Behav 9(12):01379

Article  Google Scholar 

Knapik M, Cyganek B (2019) Driver’s fatigue recognition based on yawn detection in thermal images. Neurocomputing 338:274–292

Article  Google Scholar 

Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) Eegnet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng 15(5):056013

Article  PubMed  Google Scholar 

Li R, Gao R, Suganthan PN (2023) A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition. Inf Sci 624:833–848

Article  Google Scholar 

Lu Y, Liu C, Chang F, Liu H, Huan H (2023) JHPFA-Net: joint head pose and facial action network for driver yawning detection across arbitrary poses in videos. IEEE Trans Intell Transp Syst 24(11):11850–11863

Mou L, Zhou C, Xie P, Zhao P, Jain RC, Gao W, Yin B (2021) Isotropic self-supervised learning for driver drowsiness detection with attention-based multimodal fusion. IEEE Trans Multimedia 25: 529–542

Organization WH et al. (2004) World report on road traffic injury prevention: summary. In: World report on road traffic injury prevention: summary, p 52

Othmani A, Sabri AQM, Aslan S, Chaieb F, Rameh H, Alfred R, Cohen D (2023) EEG-based neural networks approaches for fatigue and drowsiness detection: a survey. Neurocomputing 557:126709

Özçelik YB, Altan A (2023) Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust ai-based model with chaotic swarm intelligence optimization and recurrent long short-term memory. Fractal Fract 7(8):598

Article  Google Scholar 

Schmidt J, Laarousi R, Stolzmann W, Karrer-Gauß K (2018) Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera. Behav Res Methods 50:1088–1101

Article  PubMed  Google Scholar 

Subasi A, Saikia A, Bagedo K, Singh A, Hazarika A (2022) EEG-based driver fatigue detection using FAWT and multiboosting approaches. IEEE Trans Industr Inf 18(10):6602–6609

Article  Google Scholar 

Sun Z, Miao Y, Jeon JY, Kong Y, Park G (2023) Facial feature fusion convolutional neural network for driver fatigue detection. Eng Appl Artif Intell 126:106981

Article  Google Scholar 

Tsiouris KM, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI (2018) A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med 99:24–37

Article  PubMed  Google Scholar 

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30

Wang Y-K, Jung T-P, Lin C-T (2018) Theta and alpha oscillations in attentional interaction during distracted driving. Front Behav Neurosci 12:3

Article  PubMed  PubMed Central  Google Scholar 

Wang F, Wang H, Fu R (2018) Real-time ECG-based detection of fatigue driving using sample entropy. Entropy 20(3):196

Article  PubMed  PubMed Central  Google Scholar 

Wang H, Liu X, Li J, Xu T, Bezerianos A, Sun Y, Wan F (2020) Driving fatigue recognition with functional connectivity based on phase synchronization. IEEE Trans Cogn Dev Syst 13(3):668–678

Article  Google Scholar 

Wang H, Xu L, Bezerianos A, Chen C, Zhang Z (2020) Linking attention-based multiscale CNN with dynamical GCN for driving fatigue detection. IEEE Trans Instrum Meas 70:1–11

Article  Google Scholar 

Yang Y, Gao Z, Li Y, Cai Q, Marwan N, Kurths J (2019) A complex network-based broad learning system for detecting driver fatigue from EEG signals. IEEE Trans Syst, Man, Cybern: Syst 51(9):5800–5808

Article  Google Scholar 

Yan H, Ma X, Pu Z (2022) Learning dynamic and hierarchical traffic spatiotemporal features with transformer. IEEE Trans Intell Transp Syst 23(11):22386–22399

Zhang W, Wang F, Wu S, Xu Z, Ping J, Jiang Y (2020) Partial directed coherence based graph convolutional neural networks for driving fatigue detection. Rev Sci Instrum 91(7):074713

Article  CAS  PubMed  Google Scholar 

Zhang J, Liu X, Chen M, Ye Q, Wang Z (2022) Image sentiment classification via multi-level sentiment region correlation analysis. Neurocomputing 469:221–233

Article  Google Scholar 

Zheng R, Wang Z, He Y, Zhang J (2022) EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition. Cogn Neurodyn 16(2):325–336

Zou X, Li K, Chen C (2020) Multilevel attention based u-shape graph neural network for point clouds learning. IEEE Trans Industr Inf 18(1):448–456

Article  Google Scholar