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International Conference on Network and System Security

NSS 2017: Network and System Security pp 248–256Cite as

EEG-Based Random Number Generators

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10394))

Abstract

In this paper, we propose a new method that transforms electroencephalogram (EEG) signal and its wave bands into sequences of bits that can be used as a random number generator. The proposed method would be particularly useful to generate true random numbers or seeds for pseudo-random number generators. Our experiments were conducted on the EEG Alcoholism dataset and we tested the randomness using the statistical Test Suite recommended by the National Institute of Standard and Technology (NIST) for investigating the quality of random number generators, especially in cryptography application. Our experimental results show that the average success rate is \(99.02\%\) for the gamma band.

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Author information

Authors and Affiliations

  1. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, ACT, 2601, Australia

    Dang Nguyen, Dat Tran, Wanli Ma & Khoa Nguyen

Authors
  1. Dang Nguyen
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  2. Dat Tran
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  3. Wanli Ma
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  4. Khoa Nguyen
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Corresponding author

Correspondence to Dat Tran .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Zheng Yan

  2. Eurecom, Sophia Antipolos, Valbonne, France

    Refik Molva

  3. Warsaw University of Technology, Warsaw, Poland

    Wojciech Mazurczyk

  4. Aalto University, Espoo, Finland

    Raimo Kantola

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© 2017 Springer International Publishing AG

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Nguyen, D., Tran, D., Ma, W., Nguyen, K. (2017). EEG-Based Random Number Generators. In: Yan, Z., Molva, R., Mazurczyk, W., Kantola, R. (eds) Network and System Security. NSS 2017. Lecture Notes in Computer Science(), vol 10394. Springer, Cham. https://doi.org/10.1007/978-3-319-64701-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-64701-2_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64700-5

  • Online ISBN: 978-3-319-64701-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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