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On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Scott Yang Orcid Logo, Farzin Deravi

IEEE Transactions on Human-Machine Systems, Volume: 47, Issue: 6, Pages: 958 - 969

Swansea University Author: Scott Yang Orcid Logo

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DOI (Published version): 10.1109/thms.2017.2682115

Abstract

Research on using electroencephalographic signals for biometric recognition has made considerable progress and is attracting growing attention in recent years. However, the usability aspects of the proposed biometric systems in the literatures have not received significant attention. In this paper,...

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Published in: IEEE Transactions on Human-Machine Systems
ISSN: 2168-2291 2168-2305
Published: Institute of Electrical and Electronics Engineers (IEEE) 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa58936
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spelling 2021-12-30T14:31:13.6021591 v2 58936 2021-12-06 On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey 81dc663ca0e68c60908d35b1d2ec3a9b 0000-0002-6618-7483 Scott Yang Scott Yang true false 2021-12-06 SCS Research on using electroencephalographic signals for biometric recognition has made considerable progress and is attracting growing attention in recent years. However, the usability aspects of the proposed biometric systems in the literatures have not received significant attention. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of electroencephalography (EEG)-based biometric recognition. We first compare the characteristics of different stimuli that have been used for evoking biometric information bearing EEG signals. This is followed by a survey of the reported features and classifiers employed for EEG biometric recognition. To highlight the usability challenges of using EEG for biometric recognition in real-life scenarios, we propose a novel usability assessment framework which combines a number of user-related factors to evaluate the reported systems. The evaluation scores indicate a pattern of increasing usability, particularly in recent years, of EEG-based biometric systems as efforts have been made to improve the performance of such systems in realistic application scenarios. We also propose how this framework may be extended to take into account Aging effects as more performance data becomes available. Journal Article IEEE Transactions on Human-Machine Systems 47 6 958 969 Institute of Electrical and Electronics Engineers (IEEE) 2168-2291 2168-2305 13 11 2017 2017-11-13 10.1109/thms.2017.2682115 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-12-30T14:31:13.6021591 2021-12-06T22:17:03.9195895 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Scott Yang 0000-0002-6618-7483 1 Farzin Deravi 2 58936__21969__0929018a11cd469891d4595862b548bd.pdf 58936.pdf 2021-12-30T14:29:57.5222405 Output 344108 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 3.0 License true eng http://creativecommons.org/licenses/by/3.0/
title On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
spellingShingle On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
Scott Yang
title_short On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
title_full On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
title_fullStr On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
title_full_unstemmed On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
title_sort On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
author_id_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b
author_id_fullname_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b_***_Scott Yang
author Scott Yang
author2 Scott Yang
Farzin Deravi
format Journal article
container_title IEEE Transactions on Human-Machine Systems
container_volume 47
container_issue 6
container_start_page 958
publishDate 2017
institution Swansea University
issn 2168-2291
2168-2305
doi_str_mv 10.1109/thms.2017.2682115
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description Research on using electroencephalographic signals for biometric recognition has made considerable progress and is attracting growing attention in recent years. However, the usability aspects of the proposed biometric systems in the literatures have not received significant attention. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of electroencephalography (EEG)-based biometric recognition. We first compare the characteristics of different stimuli that have been used for evoking biometric information bearing EEG signals. This is followed by a survey of the reported features and classifiers employed for EEG biometric recognition. To highlight the usability challenges of using EEG for biometric recognition in real-life scenarios, we propose a novel usability assessment framework which combines a number of user-related factors to evaluate the reported systems. The evaluation scores indicate a pattern of increasing usability, particularly in recent years, of EEG-based biometric systems as efforts have been made to improve the performance of such systems in realistic application scenarios. We also propose how this framework may be extended to take into account Aging effects as more performance data becomes available.
published_date 2017-11-13T04:15:51Z
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score 11.012678

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