EEG-based brain functional connectivity dynamics in manual and video-based car-following observation among young drivers

Abbaszadeh M, Hossein-Zadeh G-A, Seyed-Allaei S, Vaziri-Pashkam M (2023) Disturbance of information in superior parietal lobe during dual-task interference in a simulated driving task. Cortex 167:235–246

Article  PubMed  Google Scholar 

Acharya JN, Acharya VJ (2019) Overview of EEG montages and principles of localization. J Clin Neurophysiol 36:325–329

Article  PubMed  Google Scholar 

Affanni A, Aminosharieh Najafi T, Guerci S (2022) Development of an EEG headband for stress measurement on driving simulators. Sensors 22:1785

Article  PubMed  PubMed Central  Google Scholar 

Almahasneh H, Chooi W-T, Kamel N, Malik AS (2014) Deep in thought while driving: an EEG study on drivers’ cognitive distraction. Transp Res Part F Traffic Psychol Behav 26:218–226. https://doi.org/10.1016/j.trf.2014.08.001

Article  Google Scholar 

Amadeo M, Campolo C, Molinaro A (2016) Information-centric networking for connected vehicles: a survey and future perspectives. IEEE Commun Mag 54:98–104

Article  Google Scholar 

Ameera A, Saidatul A, Ibrahim Z (2019) Analysis of EEG spectrum bands using power spectral density for pleasure and displeasure state. In: IOP conference series: Materials science and engineering. IOP Publishing, p 012030

Atilla F, Alimardani M (2021) EEG-based classification of drivers attention using convolutional neural network. In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS). IEEE, pp 1–4

Ba Y, Zhang W, Peng Q et al (2016) Risk-taking on the road and in the mind: behavioural and neural patterns of decision making between risky and safe drivers. Ergonomics 59:27–38

Article  PubMed  Google Scholar 

Balderston NL, Hale E, Hsiung A et al (2017) Threat of shock increases excitability and connectivity of the intraparietal sulcus. elife 6:e23608

Article  PubMed  PubMed Central  Google Scholar 

Bathla G, Bhadane K, Singh RK et al (2022) Autonomous vehicles and intelligent automation: applications, challenges, and opportunities. Mob Inf Syst 2022:7632892

Google Scholar 

Brockfeld E, Kühne RD, Wagner P (2005) Calibration and validation of microscopic models of traffic flow. Transp Res Rec 1934:179–187

Article  Google Scholar 

Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198. https://doi.org/10.1038/nrn2575

Article  CAS  PubMed  Google Scholar 

Cavanagh JF, Frank MJ (2014) Frontal theta as a mechanism for cognitive control. Trends Cogn Sci 18:414–421

Article  PubMed  PubMed Central  Google Scholar 

Chang W, Meng W, Yan G et al (2022) Driving EEG based multilayer dynamic brain network analysis for steering process. Expert Syst Appl 207:118121. https://doi.org/10.1016/j.eswa.2022.118121

Article  Google Scholar 

Chatrian GE, Lettich E, Nelson PL (1985) 10% electrode system for topographic studies of spontaneous and evoked EEG activities. Am J EEG Technol 25:83–92. https://doi.org/10.1080/00029238.1985.11080163

Article  Google Scholar 

Chen C, Hsieh J, Wu Y et al (2008) Mutual-information‐based approach for neural connectivity during self‐paced finger lifting task. Hum Brain Mapp 29:265–280. https://doi.org/10.1002/hbm.20386

Article  PubMed  PubMed Central  Google Scholar 

Chen X, Sun J, Ma Z et al (2020) Investigating the long-and short-term driving characteristics and incorporating them into car-following models. Transp Res C Emerg Technol 117:102698

Article  Google Scholar 

Dillen A, Ghaffari F, Romain O et al (2023) Optimal sensor set for decoding motor imagery from EEG. Appl Sci 13:4438. https://doi.org/10.3390/app13074438

Article  CAS  Google Scholar 

Edwards E, Soltani M, Kim W et al (2009) Comparison of time–frequency responses and the event-related potential to auditory speech stimuli in human cortex. J Neurophysiol 102:377–386

Article  PubMed  PubMed Central  Google Scholar 

Figalová N, Bieg H-J, Reiser JE et al (2024) From Driver to Supervisor: Comparing Cognitive Load and EEG-Based Attentional Resource Allocation Across Automation Levels. Int J Hum Comput Stud 182:103169

Article  Google Scholar 

Gao Z, Wang X, Yang Y et al (2019) EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation. IEEE Trans Neural Netw Learn Syst 30:2755–2763. https://doi.org/10.1109/TNNLS.2018.2886414

Article  PubMed  Google Scholar 

Gerla M, Lee E-K, Pau G, Lee U (2014) Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. 2014 IEEE world forum on internet of things (WF-IoT), IEEE, pp 241–246

Harvy J, Bezerianos A, Li J (2022) Reliability of EEG Measures in Driving Fatigue. IEEE Trans Neural Syst Rehabil Eng 30:2743–2753

Article  PubMed  Google Scholar 

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

Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132–7141

Jap BT, Lal S, Fischer P (2011) Comparing combinations of EEG activity in train drivers during monotonous driving. Expert Syst Appl 38:996–1003

Article  Google Scholar 

Kim S, Lee D (2011) Prefrontal cortex and impulsive decision making. Biol Psychiatry 69:1140–1146

Article  PubMed  Google Scholar 

Kim JY, Jeong CH, Jung MJ et al (2013) Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving. Int J Autom Technol 14:965–970

Article  Google Scholar 

Kim HS, Hwang Y, Yoon D et al (2014) Driver Workload Characteristics Analysis Using EEG Data From an Urban Road. IEEE Trans Intell Transp Syst 15:1844–1849. https://doi.org/10.1109/TITS.2014.2333750

Article  Google Scholar 

Kingma DP (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980

Kingphai K, Moshfeghi Y (2024) Mental workload assessment using deep learning models from EEG signals: a systematic review. IEEE Trans Cogn Dev Syst 17:40–60

Article  Google Scholar 

Kong W, Guo X, Zhao X et al (2011) Spectral analysis of brain function network for the classification of motor imagery tasks. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, pp 850–853

Lee J, Yang JH (2020) Analysis of driver’s EEG given take-over alarm in SAE level 3 automated driving in a simulated environment. Int J Autom Technol 21:719–728

Article  Google Scholar 

Lee E-K, Gerla M, Pau G et al (2016) Internet of Vehicles: From intelligent grid to autonomous cars and vehicular fogs. Int J Distrib Sens Netw 12:1550147716665500

Article  Google Scholar 

Li G, Yan W, Li S et al (2022) A temporal–spatial deep learning approach for driver distraction detection based on EEG signals. IEEE Trans Autom Sci Eng 19:2665–2677. https://doi.org/10.1109/TASE.2021.3088897

Article  Google Scholar 

Li P, Qi G, Yan X et al (2024) Brain Driving: Personalizing Vehicle Speed with DR-EEG Decoding and Situational Embeddings. IEEE Trans Intell Veh (Early Access). https://doi.org/10.1109/TIV.2024.3430952

Article  Google Scholar 

Li P, Qi G, Zhao S, Guan W (2025) Assessing the effects of artifacts and noise in EEG signals on car-following driving behavior prediction. Biomed Signal Process Control 100:106922. https://doi.org/10.1016/j.bspc.2024.106922

Article  Google Scholar 

Lin X, Huang Z, Ma W, Tang W (2025) EEG-based driver drowsiness detection based on simulated driving environment. Neurocomputing 616:128961. https://doi.org/10.1016/j.neucom.2024.128961

Article  Google Scholar 

Liu X, Lv L, Shen Y et al (2021) Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification. J Neural Eng 18:026003

Article  Google Scholar 

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 

Luo Z, Jin R, Shi H, Lu X (2021) Research on recognition of motor imagination based on connectivity features of brain functional network. Neural Plast 2021:1–10. https://doi.org/10.1155/2021/6655430

Article  Google Scholar 

Ma S, Yan X, Billington J et al (2024) Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomes. Accid Anal Prev 207:107769

Article  PubMed  Google Scholar 

Mesulam M-M (1999) Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos Trans R Soc Lond B Biol Sci 354:1325–1346

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ning H, Yin R, Ullah A, Shi F (2021) A survey on hybrid human-artificial intelligence for autonomous driving. IEEE Trans Intell Transp Syst 23:6011–6026

Article  Google Scholar 

Nobukawa S, Wagatsuma N, Inagaki K (2021) Gamma band functional connectivity enhanced by driving experience. In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech). IEEE, pp 379–381

Park J, Soucy E, Segawa J, Mair R, Konkle T (2024) Immersive scene representation in human visual cortex with ultra-wide-angle neuroimaging. Nat Commun 15:5477

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