EEG-Based random number generators

Dang Nguyen, Dat Tran, Wanli Ma, Khoa Nguyen

Research output: A Conference proceeding or a Chapter in BookConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication Proceedings 11th International Conference on Network and System Security (NSS 2017)
Subtitle of host publicationLecture Notes in Computer Science
EditorsZ. Yan
Place of PublicationCham, Switzerland
PublisherSpringer-Verlag London Ltd.
Pages248-256
Number of pages9
Volume10394
ISBN (Electronic)9783319647012
ISBN (Print)9783319647005
DOIs
Publication statusPublished - 26 Jul 2017
Event11th International Conference on Network and System Security, NSS 2017 - Helsinki, Finland
Duration: 21 Aug 201723 Aug 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10394 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Network and System Security, NSS 2017
CountryFinland
CityHelsinki
Period21/08/1723/08/17

Fingerprint

Random number Generator
Electroencephalography
Pseudorandom number Generator
Statistical tests
Random number
Statistical test
Cryptography
Randomness
Seed
Transform
Experimental Results
Experiment
Experiments
Electroencephalogram
Standards

Cite this

Nguyen, D., Tran, D., Ma, W., & Nguyen, K. (2017). EEG-Based random number generators. In Z. Yan (Ed.), Proceedings 11th International Conference on Network and System Security (NSS 2017): Lecture Notes in Computer Science (Vol. 10394, pp. 248-256). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10394 LNCS). Cham, Switzerland: Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-64701-2_18
Nguyen, Dang ; Tran, Dat ; Ma, Wanli ; Nguyen, Khoa. / EEG-Based random number generators. Proceedings 11th International Conference on Network and System Security (NSS 2017): Lecture Notes in Computer Science. editor / Z. Yan. Vol. 10394 Cham, Switzerland : Springer-Verlag London Ltd., 2017. pp. 248-256 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "EEG-Based random number generators",
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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Nguyen, D, Tran, D, Ma, W & Nguyen, K 2017, EEG-Based random number generators. in Z Yan (ed.), Proceedings 11th International Conference on Network and System Security (NSS 2017): Lecture Notes in Computer Science. vol. 10394, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10394 LNCS, Springer-Verlag London Ltd., Cham, Switzerland, pp. 248-256, 11th International Conference on Network and System Security, NSS 2017, Helsinki, Finland, 21/08/17. https://doi.org/10.1007/978-3-319-64701-2_18

EEG-Based random number generators. / Nguyen, Dang; Tran, Dat; Ma, Wanli; Nguyen, Khoa.

Proceedings 11th International Conference on Network and System Security (NSS 2017): Lecture Notes in Computer Science. ed. / Z. Yan. Vol. 10394 Cham, Switzerland : Springer-Verlag London Ltd., 2017. p. 248-256 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10394 LNCS).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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AB - 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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Nguyen D, Tran D, Ma W, Nguyen K. EEG-Based random number generators. In Yan Z, editor, Proceedings 11th International Conference on Network and System Security (NSS 2017): Lecture Notes in Computer Science. Vol. 10394. Cham, Switzerland: Springer-Verlag London Ltd. 2017. p. 248-256. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-64701-2_18