A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems

D Sharma; Dat Tran; Dang Nguyen

doi:10.1109/SSCI47803.2020.9308231

A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems

D Sharma, Dat Tran, Dang Nguyen

Information Systems

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

1 Citation (Scopus)

Abstract

Alcoholism is one of the brain disorders that involves in electroencephalogram (EEG) signals and have impact on EEG-based systems. However, this issue has not been investigated. In this paper, we propose an EEG-based cryptographic key generation system using EEG signals and present experiments performed on two groups of subjects, alcoholic and non-alcoholic groups in the Alcoholism database. This EEG-based cryptographic key generation system is based on an assumption that EEG signal is quasi-stationary if the time window is sufficiently short. With this assumption, stable EEG features are extracted to generate cryptographic keys. The impact of alcoholism on performance of the system is analysed based on our experimental results.

Original language	English
Title of host publication	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
Editors	Hussein Abbass, Carlos A. Coello Coello, Hemant Kumar Singh
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	79-85
Number of pages	7
ISBN (Electronic)	9781728125473
ISBN (Print)	9781728125480
DOIs	https://doi.org/10.1109/SSCI47803.2020.9308231
Publication status	Published - 1 Dec 2020
Event	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual, Canberra, Australia Duration: 1 Dec 2020 → 4 Dec 2020

Publication series

Name	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020

Conference

Conference	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
Country/Territory	Australia
City	Virtual, Canberra
Period	1/12/20 → 4/12/20

UN SDGs

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

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10.1109/SSCI47803.2020.9308231

Cite this

Sharma, D., Tran, D., & Nguyen, D. (2020). A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems. In H. Abbass, C. A. Coello Coello, & H. K. Singh (Eds.), 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 79-85). [9308231] (2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SSCI47803.2020.9308231

Sharma, D ; Tran, Dat ; Nguyen, Dang. / A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems. 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020. editor / Hussein Abbass ; Carlos A. Coello Coello ; Hemant Kumar Singh. United States : IEEE, Institute of Electrical and Electronics Engineers, 2020. pp. 79-85 (2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020).

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title = "A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems",

abstract = "Alcoholism is one of the brain disorders that involves in electroencephalogram (EEG) signals and have impact on EEG-based systems. However, this issue has not been investigated. In this paper, we propose an EEG-based cryptographic key generation system using EEG signals and present experiments performed on two groups of subjects, alcoholic and non-alcoholic groups in the Alcoholism database. This EEG-based cryptographic key generation system is based on an assumption that EEG signal is quasi-stationary if the time window is sufficiently short. With this assumption, stable EEG features are extracted to generate cryptographic keys. The impact of alcoholism on performance of the system is analysed based on our experimental results.",

keywords = "Alcoholism, Authentication, Cryptographic key generation, Data mining, EEG",

author = "D Sharma and Dat Tran and Dang Nguyen",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 ; Conference date: 01-12-2020 Through 04-12-2020",

year = "2020",

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doi = "10.1109/SSCI47803.2020.9308231",

language = "English",

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publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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booktitle = "2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020",

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Sharma, D , Tran, D & Nguyen, D 2020, A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems. in H Abbass, CA Coello Coello & HK Singh (eds), 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020., 9308231, 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 79-85, 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, Virtual, Canberra, Australia, 1/12/20. https://doi.org/10.1109/SSCI47803.2020.9308231

A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems. / Sharma, D ; Tran, Dat; Nguyen, Dang.
2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020. ed. / Hussein Abbass; Carlos A. Coello Coello; Hemant Kumar Singh. United States: IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 79-85 9308231 (2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020).

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

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T1 - A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems

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AU - Tran, Dat

AU - Nguyen, Dang

PY - 2020/12/1

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N2 - Alcoholism is one of the brain disorders that involves in electroencephalogram (EEG) signals and have impact on EEG-based systems. However, this issue has not been investigated. In this paper, we propose an EEG-based cryptographic key generation system using EEG signals and present experiments performed on two groups of subjects, alcoholic and non-alcoholic groups in the Alcoholism database. This EEG-based cryptographic key generation system is based on an assumption that EEG signal is quasi-stationary if the time window is sufficiently short. With this assumption, stable EEG features are extracted to generate cryptographic keys. The impact of alcoholism on performance of the system is analysed based on our experimental results.

AB - Alcoholism is one of the brain disorders that involves in electroencephalogram (EEG) signals and have impact on EEG-based systems. However, this issue has not been investigated. In this paper, we propose an EEG-based cryptographic key generation system using EEG signals and present experiments performed on two groups of subjects, alcoholic and non-alcoholic groups in the Alcoholism database. This EEG-based cryptographic key generation system is based on an assumption that EEG signal is quasi-stationary if the time window is sufficiently short. With this assumption, stable EEG features are extracted to generate cryptographic keys. The impact of alcoholism on performance of the system is analysed based on our experimental results.

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KW - Authentication

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KW - Data mining

KW - EEG

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Sharma D , Tran D, Nguyen D. A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems. In Abbass H, Coello Coello CA, Singh HK, editors, 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020. United States: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 79-85. 9308231. (2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020). doi: 10.1109/SSCI47803.2020.9308231

A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems

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