On the Study of Impacts of Brain Conditions on EEG-based Cryptographic Key Generation Systems

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Abstract

In this paper, we investigate impacts of brain conditions on an EEG-based cryptographic key generation system. Brain disorders, such as epilepsy and alcohol, involve in the EEG signal and hence it may have impacts on the system. This issue has not been analyzed in the literature. To solve this issue, we introduce a method for key generation from EEG signals, and implement experiments on the Australian EEG and the Alcoholism datasets. We use parametric spectrum estimate technique for feature exaction, and devised a error-correction quantization technique that is useful for a noisy data such as EEG. For experimental methodology, we perform on two groups of subjects, epileptic and non-epileptic, alcoholic and non-alcoholic to investigate the impact of brain conditions on the success rate of system. Experimental results show that both epilepsy and alcoholic actually have impacts on the performance of system.
Original languageEnglish
Title of host publication22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2018)
EditorsRobert J. Howlett, Carlos Toro, Yulia Hicks, Lakhmi C. Jain
Place of PublicationBelgrade, Serbia
PublisherElsevier B.V
Pages713-722
Number of pages10
Volume126
DOIs
Publication statusPublished - Sep 2018
Event22nd International Conference on Knowledge - Based and Intelligent Information and Engineering Systems - Metropol Palace Hotel , Belgrade, Serbia
Duration: 3 Sep 20185 Sep 2018
http://kes2018.kesinternational.org/

Publication series

NameProcedia Computer Science
PublisherElsevier
Volume126
ISSN (Electronic)1877-0509

Conference

Conference22nd International Conference on Knowledge - Based and Intelligent Information and Engineering Systems
Abbreviated titleKES 2018
CountrySerbia
CityBelgrade
Period3/09/185/09/18
Internet address

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  • Cite this

    NGUYEN, D., TRAN, D., SHARMA, D., & MA, W. (2018). On the Study of Impacts of Brain Conditions on EEG-based Cryptographic Key Generation Systems. In R. J. Howlett, C. Toro, Y. Hicks, & L. C. Jain (Eds.), 22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2018) (Vol. 126, pp. 713-722). (Procedia Computer Science; Vol. 126). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.08.005