research-article

Online Biometric Authentication Using Subject-Specific Band Power features of EEG

Authors:

Kavitha P. Thomas,

A. P. Vinod,

Neethu RobinsonAuthors Info & Claims

ICCSP '17: Proceedings of the 2017 International Conference on Cryptography, Security and Privacy

Pages 136 - 141

https://doi.org/10.1145/3058060.3058068

Published: 17 March 2017 Publication History

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Abstract

Biometric recognition of persons based on unique features extracted from brain signals is an emerging area of research nowadays, on account of the subject-specificity of human neural activity. This paper proposes an online Electroencephalogram (EEG) based biometric authentication system using band power features extracted from alpha, beta and gamma bands, when the subject is in relaxed rest state with eyes open or closed. The most distinct band features are chosen specifically for each subject which are then used to generate subject-specific template during enrollment. During online authentication, recorded test EEG pattern is matched with the respective template stored in the database and degree of matching in terms of its correlation coefficient predicts the genuineness of the claimant. A number of client and imposter authentication tests have been conducted in online framework among 6 subjects using the proposed system, and achieves an average recognition rate of 88.33% using 14 EEG channels. Experimental analysis shows the subject-specificity of distinct bands and features, and highlights the utility of subject-specific band power features in EEG-based biometric systems.

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Published In

ICCSP '17: Proceedings of the 2017 International Conference on Cryptography, Security and Privacy

March 2017

153 pages

ISBN:9781450348676

DOI:10.1145/3058060

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Wuhan Univ.: Wuhan University, China

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Association for Computing Machinery

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Published: 17 March 2017

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ICCSP '17: 2017 International Conference on Cryptography, Security and Privacy

March 17 - 19, 2017

Wuhan, China

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https://doi.org/10.1109/SPCOM60851.2024.10631606
Sasi Kumar AAithal P(2023)DeepQ Residue Analysis of Brain-Computer Classification and Prediction using Deep CNNInternational Journal of Applied Engineering and Management Letters10.47992/IJAEML.2581.7000.0179(144-163)Online publication date: 30-Jun-2023
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EEG-Based Biometric Authentication Using Gamma Band Power During Rest State

Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs

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