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Improving Driving Performance in Difficult Driving Scenarios Using Personalized Real-Time Neurofeedback

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HCI in Mobility, Transport, and Automotive Systems (HCII 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15817))

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Abstract

Excessive arousal in difficult driving scenarios is a major contributor to traffic accidents. To address this issue, this study proposes a novel method that leverages personalized neurofeedback to modulate driver arousal in real-time. Firstly, by analyzing the relationship between pupil size and reaction time, the Yerkes-Dodson law was validated. Additionally, a cascade model structure was implemented to develop an EEG-based arousal decoder, achieving an arousal recognition accuracy of 74.8%. Secondly,  the real-time generic neurofeedback was demonstrated to help a driver manage over-arousal in difficult driving scenarios. Compared to both silence and sham control conditions, the generic neurofeedback significantly extended crash-free driving time. Notably, when comparing real-time neurofeedback to silence alone, the average driving duration increased by 7.95%. Finally, instead of continuous feedback in generic neurofeedback, this study proposes a Markov decision process (MDP) framework to tailor neurofeedback strategies according to individual differences in workload and arousal. Unlike the continuous generic feedback, the MDP-based approach delivers feedback intermittently. Results indicated that the MDP-based approach further improved driving performance and stabilized arousal levels. Compared to a generic neurofeedback, the MDP strategy yielded an additional 10% increase in average driving time.

The findings highlight the feasibility of incorporating EEG-derived arousal states into an adaptive neurofeedback system, confirming that personalized neurofeedback helps drivers maintain optimal arousal under difficult driving conditions, ultimately reducing driving errors and promoting safer road performance.

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Acknowledgement

This study was supported by the National Natural Science Foundation Project (grant 52302442) and the National Natural Science Foundation Project (grant 52125208).

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Authors and Affiliations

  1. College of Transportation Engineering, Tongji University, Shanghai, China

    Jian Shi & Lishengsha Yue

  2. Shanghai Yihu Technology Co., Ltd., Shanghai, China

    Zhen Zhang

  3. Department of Bioengineering, Imperial College London, London, UK

    Tianyu Jia

  4. Sichuan Zhiling Technology Co., Ltd., Sichuan, China

    Yang Yufeng

Authors
  1. Jian Shi
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  2. Zhen Zhang
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  3. Lishengsha Yue
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  4. Tianyu Jia
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  5. Yang Yufeng
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Corresponding author

Correspondence to Lishengsha Yue .

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Editors and Affiliations

  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

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Shi, J., Zhang, Z., Yue, L., Jia, T., Yufeng, Y. (2025). Improving Driving Performance in Difficult Driving Scenarios Using Personalized Real-Time Neurofeedback. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2025. Lecture Notes in Computer Science, vol 15817. Springer, Cham. https://doi.org/10.1007/978-3-031-92689-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-92689-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-92688-4

  • Online ISBN: 978-3-031-92689-1

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