驾驶人脑电研究综述：状态感知、认知解码与人机交互协同

doi:10.16097/j.cnki.1009-6744.2026.05.001

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (5): 1-21.DOI: 10.16097/j.cnki.1009-6744.2026.05.001

• 青年基金项目成果 • 下一篇

驾驶人脑电研究综述：状态感知、认知解码与人机交互协同

奇格奇^*a,b，董镇滔^a,b，栾正凯^a,b，关伟^a,c

北京交通大学，a.综合交通运输大数据应用技术交通运输行业重点实验室；b.交通运输学院； c. 系统科学学院，北京100044

收稿日期:2026-05-01 修回日期:2026-08-19 接受日期:2026-08-26 出版日期:2026-10-25 发布日期:2026-10-21
作者简介:奇格奇（1987—），男，内蒙古赤峰人，副教授，博士。
基金资助:
国家自然科学基金青年科学基金 (72101014)；国家自然科学基金面上项目(72271018)。

Review of EEG-Based Driving Research: State Perception, Cognitive Decoding, and Human-Machine Interaction and Shared Driving

QI Geqi^*a,b, DONG Zhentao^a,b, LUAN Zhengkai^a,b, GUAN Wei^a,c

a. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport; b. School of Traffic and Transportation; c. School of Systems Science, Beijing Jiaotong University, Beijing 100044, China

Received:2026-05-01 Revised:2026-08-19 Accepted:2026-08-26 Online:2026-10-25 Published:2026-10-21
Supported by:
Young Scientists Fund of the National Natural Science Foundation of China (72101014)；General Program of the National Natural Science Foundation of China (72271018)。

摘要/Abstract

摘要： 脑电信号能够以非侵入方式表征驾驶人在连续交通任务中的注意、疲劳、工作负荷、情绪、风险感知和行为准备，为分析外显驾驶行为与内在认知过程之间的联系提供信息。本文采用系统综述方法，检索Web of Science核心合集和中国知网截至2026年4月20日收录的中英文文献，最终纳入97篇。本文以感知人、理解人和协同人为总体框架，系统综述了驾驶人状态监测、行为与意图解码、自动驾驶接管、交互信任和脑机协同控制研究，并比较了各项的实验范式、标签、脑电表征、验证协议和应用环节。现有证据表明，在紧急制动和转向等特定驾驶任务中，脑电能够在踏板或方向盘动作发生前捕捉与行为准备和动作意图相关的神经活动。相关方法已由人工特征分析扩展到时空建模、脑网络分析、跨被试泛化和多模态融合，但不同研究间在任务定义、数据规模、通道配置和验证方式方面差异较大，多数成果仍停留在离线识别阶段，脑电信息尚未稳定用于接管决策、控制权分配和交互策略调整。未来研究可聚焦可信感知与统一表征、认知-决策-控制闭环、多主体与混合交通协同，以及自然驾驶闭环验证与工程部署。本文总结驾驶人脑电由状态识别走向人机协同的现有证据、可比性边界和评价重点。

关键词: 智能交通, 脑电图, 机器学习, 驾驶行为, 人车交互, 人因工程

Abstract: Electroencephalography (EEG) provides a non-invasive means of characterizing drivers' attention, fatigue, workload, emotion, risk perception, and action preparation during continuous traffic tasks, providing information for analyzing the relationship between observable driving behavior and internal cognitive processes. This study adopted systematic review method to search the English-and Chinese-language literature from the Web of Science Core Collection and China National Knowledge Infrastructure up to April 20, 2026. Ninety-seven articles were included. This paper adopts an overarching framework of perceiving the human, understanding the human, and collaborating with the human to systematically review research on driver state monitoring, behavior and intention decoding, autonomous driving takeover, interaction trust, and brain-computer collaborative control. It further provides a comparative analysis of the experimental paradigms, labeling schemes, EEG representations, validation protocols, and application contexts across these research domains. In specific tasks such as emergency braking and steering, EEG studies have detected the neural activity related to action preparation and intention before pedal or steering-wheel movements occur. Related methodologies have evolved from handcrafted feature analysis to spatiotemporal modeling, brain network analysis, cross-subject generalization, and multimodal fusion. However, the substantial differences persist across studies with respect to task definitions, sample sizes, channel configurations, and validation protocols limit direct comparison. The majority of existing work remains confined to the offline-recognition stage, and EEG-derived information has not yet been reliably integrated into takeover decision-making, control authority allocation, and interaction strategy adaptation. Future research may focus on trustworthy perception and unified representations, the closed loops of cognition-decision-control, multi-agent and mixed traffic collaboration, as well as closed-loop validation in naturalistic driving and engineering deployment. This review summarizes the available evidence, comparability boundaries, and evaluation priorities pertaining to the evolution of EEG-based driving research from state recognition toward human-machine collaboration.

Key words: intelligent transportation, electroencephalography, machine learning, driving behavior, human vehicle interaction, human factors

中图分类号:

U491

奇格奇, 董镇滔, 栾正凯, 关伟. 驾驶人脑电研究综述：状态感知、认知解码与人机交互协同[J]. 交通运输系统工程与信息, 2026, 26(5): 1-21.

QI Geqi, DONG Zhentao, LUAN Zhengkai, GUAN Wei. Review of EEG-Based Driving Research: State Perception, Cognitive Decoding, and Human-Machine Interaction and Shared Driving[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(5): 1-21.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2026.05.001

http://www.tseit.org.cn/CN/Y2026/V26/I5/1

参考文献

[1]AVENA-KOENIGSBERGER A, MISIC B, SPORNS O. Communication dynamics in complex brain networks[J]. Nature Reviews Neuroscience, 2018, 19(1): 17-33.

[2] ZAIDAN R A, ALAMOODI A H, ZAIDAN B B, et al. Comprehensive driver behaviour review: Taxonomy, issues and challenges, motivations and research direction towards achieving a smart transportation environment[J]. Engineering Applications of Artificial Intelligence, 2022, 111: 104745.

[3] SALJOQI M, ORSINI F, ROSSI R, et al. Behavioral adaptation of human drivers in car-following interactions with automated vehicles[J]. Accident Analysis & Prevention, 2026, 231: 108514.

[4]SUN Q, ZHOU H, FU R, et al. Human-centered technology for human-machine collaboration driving system: Design and experiments[J]. Computers & Industrial Engineering, 2026, 212: 111739.

[5]WANG M, MEHROTRA S, WONG N, et al. Human machine interfaces and vehicle automation: A review of the literature and recommendations for system design, feedback, and alerts[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2024, 107: 549-561.

[6]王畅,李朝,赵霞,等.基于图像和深度学习的驾驶人状态监测算法综述[J]. 中国公路学报, 2025, 38(1): 324-347. [WANG C, LI Z, ZHAO X, et al. Review of image and deep learning based algorithms for driver state monitoring[J]. China Journal of Highway and Transport, 2025, 38(1): 324-347.]

[7]JU J, LI H. A survey of EEG-based driver state and behavior detection for intelligent vehicles[J]. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2024, 6(3): 420-434.

[8]JIANG K, YANG W, TANG X, et al. Mechanisms behind hazard recognition in potential rear-end collisions: An EEG study of cross-frequency phase synchrony in complex brain networks[J]. Expert Systems with Applications, 2025, 259: 125298.

[9] ZHANG C, ESKANDARIAN A. A survey and tutorial of EEG-based brain monitoring for driver state analysis[J]. IEEE/CAA Journal of Automatica Sinica, 2021, 8(7): 1222-1242.

[10] ALI M U, ZAFAR A, KIM S H, et al. Intelligent driver drowsiness detection using brain imaging: A systematic review for enhanced road safety in intelligent transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2026, 27(7): 7375 7396.

[11] TOMSON M, MATHEW T V, VELAGA N R. A systematic review of studies investigating the impact of sleep deprivation on drivers' physiology and driving performance[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2025, 109: 458-479.

[12] 褚万里, 郭鹏,章捷,等.机动车驾驶员疲劳驾驶检测方法研究综述[J]. 电子设计工程,2025, 33(4): 36-41. [CHU W L, GUO P, ZHANG J, et al. Review of research on fatigue driving detection methods for motor vehicle drivers[J]. Electronic Design Engineering, 2025, 33(4): 36-41.]

[13] KINGPHAI K, MOSHFEGHI Y. Mental workload assessment using deep learning models from EEG signals: A systematic review[J]. IEEE Transactions on Cognitive and Developmental Systems, 2025, 17(1): 40-60.

[14] EL-NABI S A, EL-SHAFAI W, EL-RABAIE E S M, et al. Machine learning and deep learning techniques for driver fatigue and drowsiness detection: A review[J]. Multimedia Tools and Applications, 2024, 83(3): 9441 9477.

[15] LI G, YUAN Y, OUYANG D, et al. Driver distraction from the EEG perspective: A review[J]. IEEE Sensors Journal, 2024, 24(3): 2329-2349.

[16] ALBADAWI Y, TAKRURI M, AWAD M. A review of recent developments in driver drowsiness detection systems[J]. Sensors, 2022, 22(5): 2069.

[17] WATLING C N, MAHMUDUL H M, LARUE G S. Sensitivity and specificity of the driver sleepiness detection methods using physiological signals: A systematic review[J]. Accident Analysis & Prevention, 2021, 150: 105900.

[18] 许子明, 牛一帆,温旭云,等.基于脑电信号的认知负荷评估综述[J]. 航天医学与医学工程, 2021, 34(4): 339-348. [XU Z M, NIU Y F, WEN X Y, et al. A review of cognitive workload assessment based on EEG[J]. Space Medicine & Medical Engineering, 2021, 34(4): 339-348.]

[19] 关伟, 杨柳, 江世雄,等.脑电在交通驾驶行为中的应用研究综述[J]. 交通运输系统工程与信息,2016,16 (3): 35-44. [GUAN W, YANG L, JIANG S X, et al. Review on the application of EEG in traffic driving behavior study[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(3): 35-44.]

[20] YANG Y, GAO Z, LI Y, 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.

[21] XU T, WANG H, LU G, et al. E-Key: An EEG-based biometric authentication and driving fatigue detection system[J]. IEEE Transactions on Affective Computing, 2023, 14(2): 864-877.

[22] HU F, ZHANG L, YANG X,et al. EEG-based driver fatigue detection using spatio-temporal fusion network with brain region partitioning strategy[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8): 9618-9630.

[23] HU X, LODEWIJKS G. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue[J]. Journal of Safety Research, 2020, 72: 173 187.

[24] CHAUDHURI A, ROUTRAY A. Driver fatigue detection through chaotic entropy analysis of cortical sources obtained from scalp EEG signals[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(1): 185 198.

[25] 张冰涛,常文文,李秀兰.基于时空脑电特征与并行神经网络的疲劳驾驶检测[J].交通运输系统工程与信息, 2023, 23(2): 315-325. [ZHANG B T, CHANG W W, LI X L. Fatigue driving detection based on spatial temporal electroencephalogram features and parallel neural networks[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 315-325.]

[26] GAO Z, WANG X, YANG Y, et al. EEG-based spatio temporal convolutional neural network for driver fatigue evaluation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(9): 2755-2763.

[27] DU G, WANG Z, LI C, et al. A TSK-type convolutional recurrent fuzzy network for predicting driving fatigue[J]. IEEE Transactions on Fuzzy Systems, 2021, 29(8): 2100 2111.

[28] 孙家政, 李卫民,张宁玲,等.基于前额脑电渐近层次融合网络的疲劳驾驶检测方法[J].生物医学工程学杂志, 2025, 42(3): 544-551. [SUN J Z, LI W M, ZHANG N L, et al. Fatigue driving detection based on prefrontal electroencephalogram asymptotic hierarchical fusion network[J]. Journal of Biomedical Engineering, 2025, 42 (3): 544-551.]

[29] 冯笑, 代少升,黄炼.基于可解释深度学习的单通道脑电跨被试疲劳驾驶检测[J].仪器仪表学报,2023, 44(5): 140-149. [FENG X, DAI S S, HUANG L. Cross subject driver fatigue detection from single-channel EEG with an interpretable deep learning model[J]. Chinese Journal of Scientific Instrument, 2023, 44(5): 140-149.]

[30] WANG H, XU L, BEZERIANOS A, et al. Linking attention-based multiscale CNN with dynamical GCN for driving fatigue detection[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-11.

[31] ZHU J, LIU Q, LIU H, et al. STP-Net: Spatiotemporal graph neural network incorporating global brain functional network partitioning for driver fatigue monitoring[J]. Knowledge-Based Systems, 2025, 329: 114294.

[32] CUI J, LAN Z, SOURINA O, et al. EEG-based cross subject driver drowsiness recognition with an interpretable convolutional neural network[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(10): 7921-7933.

[33] LI G, ZHANG L, OUYANG D, et al. Cross-driver domain generalization for improved drowsiness recognition based on EEG signals[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(10): 14704-14721.

[34] 王洪涛, 殷浩钧,陈创泉,等.基于脑电信号的驾驶疲劳检测综述[J]. 华中科技大学学报(自然科学版), 2022, 50(11): 54-65, 78. [WANG H T, YIN H J, CHEN C Q, et al. Review on driving fatigue detection based on EEG[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2022, 50(11): 54 65, 78.]

[35] ZHANG Z, NING H, ZHOU F. A systematic survey of driving fatigue monitoring[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 19999 20020.

[36] GOLDSWORTHY J, WATLING C N, ROSE C, et al. The effects of distraction on younger drivers: A neurophysiological perspective[J]. Applied Ergonomics, 2024, 114: 104147.

[37] LI G, YAN W, LI S, et al. A temporal-spatial deep learning approach for driver distraction detection based on EEG signals[J]. IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 2665-2677.

[38] SHI C, YAN F, ZHANG J, et al. Right superior frontal involved in distracted driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2023, 93: 191-203.

[39] QI G, LIU R, GUAN W, et al. Augmented recognition of distracted driving state based on electrophysiological analysis of brain network[J]. Cyborg and Bionic Systems, 2024, 5: 0130.

[40] MA S, YAN X, BILLINGTON J, et al. Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomes[J]. Accident Analysis & Prevention, 2024, 207: 107769.

[41] 华强, 金立生,郭柏苍,等.一种混行环境下驾驶人认知分心识别方法[J].吉林大学学报(工学版),2022,52 (8): 1800-1807. [HUA Q, JIN L S, GUO B C, et al. A recognition method for driver's cognitive distraction in simulated mixed traffic environment[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(8): 1800-1807.]

[42] 杨艳群, 黄永健,罗秀玲,等.认知分心情境下驾驶人应激反应行为研究[J].重庆交通大学学报(自然科学版), 2024, 43(3): 54-63, 115. [YANG Y Q, HUANG Y J, LUO X L, et al. Driver's stress response behavior in cognitive distraction condition[J]. Journal of Chongqing Jiaotong University (Natural Science), 2024, 43(3): 54 63, 115.]

[43] ZHANG G, ETEMAD A. Capsule attention for multimodal EEG-EOG representation learning with application to driver vigilance estimation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1138-1149.

[44] ZUO X, ZHANG C, CONG F, et al. Mobile phone use driver distraction detection based on MSaE of multi modality physiological signals[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(11): 17650 17665.

[45] FIGALOVA N, BIEG H J, REISER J E, et al. From driver to supervisor: Comparing cognitive load and EEG based attentional resource allocation across automation levels[J]. International Journal of Human-Computer Studies, 2024, 182: 103169.

[46] HE D, DONMEZ B, LIU C C, et al. High cognitive load assessment in drivers through wireless electroencephalography and the validation of a modified N-back task[J]. IEEE Transactions on Human-Machine Systems, 2019, 49(4): 362-371.

[47] ANGKAN P, BEHINAEIN B, MAHMUD Z, et al. Multimodal brain-computer interface for in-vehicle driver cognitive load measurement: Dataset and baselines [J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(6): 5949-5964.

[48] ATICI-ULUSU H, TASKAPILIOGLU O, GUNDUZ T. A neuroergonomics approach to investigate the mental workload of drivers in real driving settings[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2024, 103: 177-189.

[49] 高明星, 季天行,王海晓,等.草原公路人机共驾环境下驾驶人接管负荷研究[J].内蒙古农业大学学报(自然科学版),2024, 45(4): 37-45. [GAO M X, JI T X, WANG HX, et al. Driver take-over loads in the human-machine co-operative environment on a grassland highway[J]. Journal of Inner Mongolia Agricultural University (Natural Science Edition), 2024, 45(4): 37-45.]

[50] 刘天程, 常若松,解芳,等.基于脑电信号精准识别特种车辆作业人员视听通道工作负荷[J].兵工学报, 2025, 46(5): 240815. [LIU T C, CHANG R S, XIE F, et al. Identification of auditory and visual channel workloads of special vehicle operators based on EEG indicators[J]. Acta Armamentarii, 2025, 46(5): 240815.]

[51] 杨艳群,王林伟,赵晓华,等.基于标志信息、形式与驾驶人认知负荷的高速公路路网指路标志综合协调性研究[J]. 中国公路学报,2025,38(11): 342-361. [YANG Y Q, WANG L W, ZHAO X H, et al. Research on the comprehensive coordination of freeway network guide signs based on sign information, format, and driver cognitive load[J]. China Journal of Highway and Transport, 2025, 38(11): 342-361.]

[52] CHENG S, DONG J. IMCATN: Transformer based learning with multi-scale information fusion for driver cognitive load recognition[J]. International Journal of Human-Computer Interaction, 2026, 42(8): 6273-6291.

[53] LIU T, ZHANG Y, LI X, et al. An in-vehicle digital representation model for identifying the channel and level of driver workload based on EEG signals[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(10): 15838-15851.

[54] YANG L, ZHOU R, LI G, et al. Recognizing and explaining driving stress using a shapley additive explanation model by fusing EEG and behavior signals [J]. Accident Analysis & Prevention, 2025, 209: 107835.

[55] CHEN J, LIN X, MA W, et al. EEG-based emotion recognition for road accidents in a simulated driving environment[J]. Biomedical Signal Processing and Control, 2024, 87: 105411.

[56] KIM H, KIM S, KIM H, et al. Modulation of driver's emotional states by manipulating in-vehicle environment: Validation with biosignals recorded in an actual car environment[J]. IEEE Transactions on Affective Computing, 2022, 13(4): 1783-1792.

[57] ZHANG W, QIN Y, ZHANG S, et al. How EEG-based cross-subject driving emotion is recognized: A multi source transfer manifold learning model[J]. Biomedical Signal Processing and Control, 2026, 112: 108454.

[58] HABIBIFAR N, SALMANZADEH H. Relationship between driving styles and biological behavior of drivers in negative emotional state[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 85: 245 258.

[59] 万平, 吴超仲,林英姿,等.基于置信规则库的驾驶人愤怒情绪识别模型[J]. 交通运输系统工程与信息, 2015, 15(5): 96-102. [WAN P, WU C Z, LIN Y Z, et al. A recognition model of driving anger based on belief rule base[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 96-102.]

[60] HU F, YAO P, HE K, et al. Effects of emotional olfactory stimuli on modulating angry driving based on an EEG connectivity study[J]. International Journal of Neural Systems, 2024, 34(11): 2450058.

[61] PENG Y, ZHANG Z, JIANG Z, et al. Driver emotion regulation via ambient lighting and music: An EEG based ergonomic evaluation for intelligent cockpit[J]. Alexandria Engineering Journal, 2026, 138: 51-65.

[62] 杜义浩,李菁金,孙梦雨,等.基于空间-时间多模态域对抗模型的驾驶员情绪识别研究[J].计量学报,2025, 46(8): 1218-1224. [DU Y H, LI J J, SUN M Y, et al. A study on driver emotion recognition based on multimodal spatial temporal attention neural network with domain discriminator model[J]. Acta Metrologica Sinica, 2025, 46 (8): 1218-1224.]

[63] JU J, BI L, FELEKE A G. Noninvasive neural signal based detection of soft and emergency braking intentions of drivers[J]. Biomedical Signal Processing and Control, 2022, 72: 103330.

[64] LI J, LIANG X, LIU Y, et al. Decoding braking intentions from EEG signals: A convolution-transformer network approach[J]. IEEE Sensors Journal, 2024, 24(22): 38329 38343.

[65] 唐帮备,朱明鑫,郭钢.基于脑电信号的驾驶员意图与风险驾驶状态识别综述[J].汽车工程学报,2025,15 (5): 638-653. [TANG B B, ZHU M X, GUO G. A review on EEG-based recognition of driver intention and risky driving states[J]. Chinese Journal of Automotive Engineering, 2025, 15(5): 638-653.]

[66] TENG T, BI L, LIU Y. EEG-based detection of driver emergency braking intention for brain-controlled vehicles [J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(6): 1766-1773.

[67] BI L, WANG H, TENG T, et al. A novel method of emergency situation detection for a brain-controlled vehicle by combining EEG signals with surrounding information[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(10): 1926 1934.

[68] CHANG W, MENG W, YAN G, et al. Driving EEG based multilayer dynamic brain network analysis for steering process[J]. Expert Systems with Applications, 2022, 207: 118121.

[69] LIU Y, LIANG X, YU Y, et al. Recognizing drivers' turning intentions with EEG and eye movement[J]. Biomedical Signal Processing and Control, 2025, 101: 107218.

[70] YANG L, MA R, ZHANG H M, et al. Driving behavior recognition using EEG data from a simulated car following experiment[J]. Accident Analysis & Prevention, 2018, 116: 30-40.

[71] DONG N, WU Z, ZHANG W, et al. Intention-prioritized fuzzy fusion control for BCI-based autonomous vehicles [J]. Biomedical Signal Processing and Control, 2024, 87: 105486.

[72] SUN J, ZHOU Z, LIU Y, et al. Decoding driving intentions via a novel brain-computer interface paradigm with low cognitive load and high robustness[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2026, 56(5): 3235-3249.

[73] LIU T, QIN Y, ZHANG S, et al. A diffusion-based feature enhancement approach for driving behavior classification with EEG data[J]. Advanced Engineering Informatics, 2025, 65: 103279.

[74] ZHANG W, QIN Y, TAO X. MMPHGCN: A hypergraph convolutional network for detection of driver intention on multimodal physiological signals[J]. IEEE Signal Processing Letters, 2024, 31: 1730-1734.

[75] YAN F, LIU M, DING C, et al. Driving style recognition based on electroencephalography data from a simulated driving experiment[J]. Frontiers in Psychology, 2019, 10: 1254.

[76] ZHAO S, GUAN W, QI G, et al. Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task[J]. Accident Analysis & Prevention, 2022, 171: 106665.

[77] LI P, QI G, ZHAO S, et al. EEG-based brain functional connectivity dynamics in manual and video-based car following observation among young drivers[J]. Cognitive Neurodynamics, 2026, 20(1): 72.

[78] AGRAWAL S, PEETA S. Evaluating the impacts of driver's pre-warning cognitive state on takeover performance under conditional automation[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 83: 80-98.

[79] DENG M, GLUCK A, ZHAO Y, et al. An analysis of physiological responses as indicators of driver takeover readiness in conditionally automated driving[J]. Accident Analysis & Prevention, 2024, 195: 107372.

[80] HUANG H, HUANG J, ZHOU Z, et al. Risk assessment and prediction of conditional driving automation takeover: Considering the impact of driver state[J]. Cognition, Technology & Work, 2025, 27(3): 563-578.

[81] SEET M, DRAGOMIR A, HARVY J, et al. Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation [J]. Accident Analysis & Prevention, 2022, 168: 106588.

[82] DENG L, LI Y, FAN H, et al. Who's in charge: Drivers or voice assistants? How power dynamics shape emotions and takeover behavior in automated driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2026, 119: 103569.

[83] QI G, ZHAO S, YU J, et al. Recognizing autonomous driving disengagement scenarios using the transferable knowledge from human driver's EEG cognitive data[J]. Accident Analysis & Prevention, 2025, 219: 108102.

[84] SEET M, HARVY J, BOSE R, et al. Differential impact of autonomous vehicle malfunctions on human trust[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(1): 548-557.

[85] XU T, DRAGOMIR A, LIU X, et al. An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis[J]. Frontiers in Neuroinformatics, 2022, 16: 907942.

[86] ZHANG T, YANG J, CHEN M, et al. EEG-based assessment of driver trust in automated vehicles[J]. Expert Systems with Applications, 2024, 246: 123196.

[87] MA Z, ZHANG Y. Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: Effects of automated driving styles and driver's driving styles[J]. Accident Analysis & Prevention, 2021, 159: 106238.

[88] QI G, DONG Z, GUAN W, et al. Human-machine fitness while driving: Evaluating human acceptance of autonomous driving styles through EEG signal analysis [J]. IEEE Transactions on Human-Machine Systems, 2026, 56(3): 636-649.

[89] LUKOVICS M, PRONAY S, MAJO-PETRI Z, et al. Combining survey-based and neuroscience measurements in customer acceptance of self-driving technology[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2023, 95: 46-58.

[90] ZHANG Y, ZHAO H, HU C, et al. Mitigation of motion sickness and optimization of motion comfort in autonomous vehicles: Systematic survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(12): 21737-21756.

[91] LU Y, BI L. EEG signals-based longitudinal control system for a brain-controlled vehicle[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(2): 323-332.

[92] XU Z, BI L, YANG Z, et al. Brain-controlled operator model-driven deep reinforcement learning for adaptive brain-machine collaborative control[J]. Expert Systems with Applications, 2026, 305: 130770.

[93] ZHUANG J, GENG K, YIN G. Ensemble learning based brain-computer interface system for ground vehicle control[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(9): 5392-5404.

[94] LU Y, BI L. Combined lateral and longitudinal control of EEG signals-based brain-controlled vehicles[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(9): 1732-1742.

[95] LI P, QI G, YAN X, et al. Brain driving: Personalizing vehicle speed with DR-EEG decoding and situational embeddings[J]. IEEE Transactions on Intelligent Vehicles, 2025, 10(3): 1658-1677.

[96] SHIN J H, KWON J, KIM J U, et al. Wearable EEG electronics for a brain-AI closed-loop system to enhance autonomous machine decision-making[J]. npj Flexible Electronics, 2022, 6(1): 32.

[97] LI H, BI L, LI X, et al. Robust predictive control for EEG based brain-robot teleoperation[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8): 9130 9140.

驾驶人脑电研究综述：状态感知、认知解码与人机交互协同

Review of EEG-Based Driving Research: State Perception, Cognitive Decoding, and Human-Machine Interaction and Shared Driving

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	杨静文, 齐旭东, 王萍, 夏新海. 智能网联环境下多簇交通波消散的车辆群体协同控制方法[J]. 交通运输系统工程与信息, 2026, 26(5): 79-88.
[2]	陈坚, 胡功明, 简洪波, 傅志妍. 基于社交媒体数据的交通突发事件提取方法[J]. 交通运输系统工程与信息, 2026, 26(5): 122-131.
[3]	翁金贤 , 郭豪飞, 丁海峰. 基于驾驭工程的临时集卡专用道作业区粒流协同控制方法[J]. 交通运输系统工程与信息, 2026, 26(5): 173-183.
[4]	杜凯, 刘会洋, 何启航, 于艳波, 陈丹, 宋京妮. 融合车辆轨迹预测的高速匝道自动驾驶车辆汇入控制方法[J]. 交通运输系统工程与信息, 2026, 26(5): 184-196.
[5]	邹复民, 江林文, 蔡祈钦, 廖律超, 赖树坤. 考虑轨迹语义与时空关联的高速公路行程时间预测方法[J]. 交通运输系统工程与信息, 2026, 26(5): 227-237.
[6]	张建华, 陈云帅. 面向时空约束的网联车匝道合流分层协同控制[J]. 交通运输系统工程与信息, 2026, 26(5): 238-247.
[7]	程国柱, 罗天健, 王文志, 梁嘉祺, 冯天军. 城市快速路施工区上游智能网联汽车协同换道方法研究[J]. 交通运输系统工程与信息, 2026, 26(5): 386-402.
[8]	薛晴婉, 张先喆, 杨林溢, 闫睿轩, 王力. 辅助驾驶环境下融合驾驶行为熵的交叉口碰撞风险预测[J]. 交通运输系统工程与信息, 2026, 26(4): 112-123.
[9]	贾彦峰, 李富贵, 曲大义, 胡星辰, 李妍妍. 人工势场增强的网联异构车队分布式非线性纵横向耦合控制[J]. 交通运输系统工程与信息, 2026, 26(4): 148-161.
[10]	何永明, 那浩轩, 金玉凤, 张龙龙. 考虑交通流态势的自动驾驶车辆换道轨迹模型[J]. 交通运输系统工程与信息, 2026, 26(4): 162-172.
[11]	王昊, 陈旭梅. 考虑异质驾驶行为的智能网联车辆超车应对策略研究[J]. 交通运输系统工程与信息, 2026, 26(4): 173-187.
[12]	马千里, 焦乙恩, 周屹恒, 贾鹏. 机场混编摆渡车队高峰小时服务调度优化[J]. 交通运输系统工程与信息, 2026, 26(4): 321-331.
[13]	黄凯, 谢子俊, 李濠宇, 刘芯彤, 刘志远. 基于锚点分类改进的自动驾驶车道线检测研究[J]. 交通运输系统工程与信息, 2026, 26(3): 14-24.
[14]	王正礼, 唐子墨, 郑振杰. 融合检索增强生成与思维链的交通事故责任认定大模型构建[J]. 交通运输系统工程与信息, 2026, 26(3): 83-92.
[15]	姜晓红, 仲韫豪, 肖靖沂, 邢吉平, 李家伟, 华晶雯. 考虑即时零售订单的城乡公交响应式客货联运调度[J]. 交通运输系统工程与信息, 2026, 26(3): 156-165.