Yousefi, F and Kolivand, H (2021) A New Solution to the Brain State Permanency for Brain-Based Authentication Methods. In: 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) (1). pp. 69-72. (1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), Riyadh, Saudi Arabia, 6th Apr- 7th Apri 2021).

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Abstract

Nowadays, to access any digital device we use authentication techniques, which is a critical technology in terms of security. Present biometric authentications such as fingerprints or face recognition are the most used methods in our digitalized world, which are impressively advantageous in terms of security. However, there are still some flaws in using these methods like not being useful for physical disabilities, environment usage matters, and most importantly the possibility of replicating them with some new technologies because of their visibility. Brain signal is another human biometric that could cover the issues of other types in terms of security and visibility. There are different perspectives about the EEG authentication challenges, including ease of use, privacy, and confirmation necessities like comprehensiveness, uniqueness, collectability, and most importantly permanency which is a big challenge for EEG-based authentications specifically. In this paper, we proposed a method using the deep breath strategy to use brain signals for authentication purposes regardless of brain situation. The result shows that our proposal accomplishment can alter the entire cycle of brain-based authentication when compared with other techniques and EEG-based authentication methods according to the parameter of permanency of the technique in many different brain states.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	IEEE
Related URLs:	https://ieeexplore.ieee.org/abstract/document/9425075
SWORD Depositor:	A Symplectic
Date Deposited:	03 Feb 2025 14:48
Last Modified:	03 Feb 2025 14:48
DOI or ID number:	10.1109/CAIDA51941.2021.9425075
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25512

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