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Mining the Potential Temporal Features Based on Wearable EEG Signals for Driving State Analysis

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Computational Data and Social Networks (CSoNet 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14479))

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Abstract

Fatigue driving is considered to be one of the main factors causing traffic accidents, so fatigue driving detection technology has an important role in road safety. Currently, EEG-based detection is one of the most intuitive and effective means for fatigue driving. We introduce a model known as EFDD (EEG-based Fatigue Driving Detection Model), in our study, by analyzing EEG signals, we extract time-domain and frequency-domain features respectively, explore the potential of different temporal EEG features for fatigue driving detection, Classification using LightGBM machine learning models, and then realize fatigue driving detection. Experiments demonstrate that our extracted features perform well in fatigue driving detection. Meanwhile, our study provides technical support for the feasibility of applying portable detection devices in the future.

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Acknowledgment

This work was supported by the Science and Technology Development Plan of Jilin Province, China (Grant No. 20220402033GH).

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Authors and Affiliations

  1. Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin, China

    Ling Wang, Fangjie Song, Tie Hua Zhou, Chunxu Yang & Wanlin Zhang

Authors
  1. Ling Wang
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  2. Fangjie Song
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  3. Tie Hua Zhou
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  4. Chunxu Yang
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  5. Wanlin Zhang
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Corresponding author

Correspondence to Tie Hua Zhou .

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Editors and Affiliations

  1. National Economics University, Hanoi, Vietnam

    Minh Hoàng Hà

  2. Florida Atlantic University, Boca Raton, FL, USA

    Xingquan Zhu

  3. University of Florida, Gainesville, FL, USA

    My T. Thai

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wang, L., Song, F., Zhou, T.H., Yang, C., Zhang, W. (2024). Mining the Potential Temporal Features Based on Wearable EEG Signals for Driving State Analysis. In: Hà, M.H., Zhu, X., Thai, M.T. (eds) Computational Data and Social Networks. CSoNet 2023. Lecture Notes in Computer Science, vol 14479. Springer, Singapore. https://doi.org/10.1007/978-981-97-0669-3_9

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  • DOI: https://doi.org/10.1007/978-981-97-0669-3_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0668-6

  • Online ISBN: 978-981-97-0669-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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