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Driving Mental Fatigue Classification Based on Brain Functional Connectivity

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Engineering Applications of Neural Networks (EANN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

EEG techniques have been widely used for mental fatigue monitoring, which is an important factor for driving safety. In this work, we performed an experiment involving one hour driving simulation. Based on EEG recordings, we created brain functional networks in alpha power band with three different methods, partial directed coherence (PDC), direct transfer function (DTF) and phase lag index (PLI). Then, we performed feature selection and classification between alertness and fatigue states, using the functional connectivity as features. High accuracy (84.7%) was achieved, with 22 discriminative connections from PDC network. The selected features revealed alterations of the functional network due to mental fatigue and specifically reduction of information flow among areas. Finally, a feature ranking is provided, which can lead to electrode minimization for real-time fatigue monitoring applications.

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Acknowledgment

This work was supported by the National University of Singapore (NUS) and Defense Science Organization (DSO) for Cognitive Engineering Group at Singapore Institute for Neurotechnology (SINAPSE).

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Authors and Affiliations

  1. Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, 28 Medical Dr. #05-COR, Singapore, 117456, Singapore

    Georgios N. Dimitrakopoulos, Ioannis Kakkos, Junhua Li, Yu Sun & Anastasios Bezerianos

  2. Department of Electrical and Computer Engineering, University of Patras, 25600, Patras, Greece

    Georgios N. Dimitrakopoulos & Kyriakos Sgarbas

  3. School of Medicine, University of Patras, 25600, Patras, Greece

    Aristidis G. Vrahatis & Anastasios Bezerianos

  4. Computer Engineering and Informatics Department, University of Patras, 25600, Patras, Greece

    Aristidis G. Vrahatis

Authors
  1. Georgios N. Dimitrakopoulos
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  2. Ioannis Kakkos
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  3. Aristidis G. Vrahatis
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  4. Kyriakos Sgarbas
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  5. Junhua Li
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  6. Yu Sun
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  7. Anastasios Bezerianos
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Corresponding author

Correspondence to Anastasios Bezerianos .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Dimitrakopoulos, G.N. et al. (2017). Driving Mental Fatigue Classification Based on Brain Functional Connectivity. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_39

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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