A Study on Combing EEG signals and Crytography for Bitcoin security

Binh Nguyen, Dat Tran, Wanli Ma

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review


In this paper, we propose two EEG-based systems including cryptographic key generation and (true) random number generation to enhance Bitcoin security. The first system is based on the quasi-stationary characteristic of EEG signals when analyzed in a sufficient short time window. With this quasi-stationary, stable EEG feaures are extracted and corrected to generate cryptographic keys from EEG-based authentication for the protection of Bitcoin wallets. The second one is based on the non-linear and chaotic characteristics of EEG signals. By mathematical transformation, EEG signals can be transformed to be random binary sequences for the use of protecting digital signatures in Bitcoin transactions. Two EEG datasets which are DEAP and GrazIIIa were used to validate the performance of the proposed system. Our experimental results showed that both cryptographic keys and random numbers are securely derived with very high success rates and very high rates of passing the standard statistical tests recommended by the National Institute of Standard and Technology (NIST) for examining the quality of randomness, especially in cryptography applications.
Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part IV
EditorsTom Gedeon, Kok Wai Wong, Minho Lee
Place of PublicationSwitzerland
Number of pages9
ISBN (Electronic)9783030368081
ISBN (Print)9783030368074
Publication statusPublished - Dec 2019
Event26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia
Duration: 12 Dec 201915 Dec 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference26th International Conference on Neural Information Processing, ICONIP 2019


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