EEG-Based random number generators

Dang Nguyen; Dat Tran; Wanli Ma; Khoa Nguyen

doi:10.1007/978-3-319-64701-2_18

EEG-Based random number generators

Dang Nguyen
, Dat Tran
, Wanli Ma
, Khoa Nguyen

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

9 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 language	English
Title of host publication	Proceedings 11th International Conference on Network and System Security (NSS 2017)
Subtitle of host publication	Lecture Notes in Computer Science
Editors	Z. Yan
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	248-256
Number of pages	9
Volume	10394
ISBN (Electronic)	9783319647012
ISBN (Print)	9783319647005
DOIs	https://doi.org/10.1007/978-3-319-64701-2_18
Publication status	Published - 26 Jul 2017
Event	11th International Conference on Network and System Security, NSS 2017 - Helsinki, Finland Duration: 21 Aug 2017 → 23 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10394 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Network and System Security, NSS 2017
Country/Territory	Finland
City	Helsinki
Period	21/08/17 → 23/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-319-64701-2_18

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). Springer. https://doi.org/10.1007/978-3-319-64701-2_18

Nguyen, Dang ; Tran, Dat ; Ma, Wanli et al. / 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, 2017. pp. 248-256 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{69c124b013124297bd4ba8055d4f6705,

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.",

keywords = "EEG, NIST test suite, Random number generator",

author = "Dang Nguyen and Dat Tran and Wanli Ma and Khoa Nguyen",

year = "2017",

month = jul,

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doi = "10.1007/978-3-319-64701-2\_18",

language = "English",

isbn = "9783319647005",

volume = "10394",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "248--256",

editor = "Z. Yan",

booktitle = "Proceedings 11th International Conference on Network and System Security (NSS 2017)",

address = "Netherlands",

note = "11th International Conference on Network and System Security, NSS 2017 ; Conference date: 21-08-2017 Through 23-08-2017",

}

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, 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 et al.
Proceedings 11th International Conference on Network and System Security (NSS 2017): Lecture Notes in Computer Science. ed. / Z. Yan. Vol. 10394 Cham, Switzerland: Springer, 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 Book › Conference contribution › peer-review

TY - GEN

T1 - EEG-Based random number generators

AU - Nguyen, Dang

AU - Tran, Dat

AU - Ma, Wanli

AU - Nguyen, Khoa

PY - 2017/7/26

Y1 - 2017/7/26

N2 - 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.

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.

KW - EEG

KW - NIST test suite

KW - Random number generator

UR - https://www.scopus.com/pages/publications/85028474463

U2 - 10.1007/978-3-319-64701-2_18

DO - 10.1007/978-3-319-64701-2_18

M3 - Conference contribution

AN - SCOPUS:85028474463

SN - 9783319647005

VL - 10394

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 248

EP - 256

BT - Proceedings 11th International Conference on Network and System Security (NSS 2017)

A2 - Yan, Z.

PB - Springer

CY - Cham, Switzerland

T2 - 11th International Conference on Network and System Security, NSS 2017

Y2 - 21 August 2017 through 23 August 2017

ER -

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. 2017. p. 248-256. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-64701-2_18

EEG-Based random number generators

Abstract

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