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Task sensitivity in EEG biometric recognition

Scott Yang Orcid Logo, Farzin Deravi, Sanaul Hoque

Pattern Analysis and Applications, Volume: 21, Issue: 1, Pages: 105 - 117

Swansea University Author: Scott Yang Orcid Logo

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DOI (Published version): 10.1007/s10044-016-0569-4

Abstract

This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who perfo...

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Published in: Pattern Analysis and Applications
ISSN: 1433-7541 1433-755X
Published: Springer Science and Business Media LLC 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa58935
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first_indexed 2021-12-06T22:15:40Z
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spelling 2021-12-30T14:35:22.7735187 v2 58935 2021-12-06 Task sensitivity in EEG biometric recognition 81dc663ca0e68c60908d35b1d2ec3a9b 0000-0002-6618-7483 Scott Yang Scott Yang true false 2021-12-06 SCS This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who performed four different motor movement/imagery tasks while their data were recorded. Training and test of the system was performed using a number of experimental protocols to establish if training with one type of task and tested with another would significantly affect the recognition performance. Also, experiments were conducted to evaluate the performance when a mixture of data from different tasks was used for training. The results suggest that performance is not significantly affected when there is a mismatch between training and test tasks. Furthermore, as the amount of data used for training is increased using a combination of data from several tasks, the performance can be improved. These results indicate that a more flexible approach may be incorporated in data collection for EEG-based biometric systems which could facilitate their deployment and improved performance. Journal Article Pattern Analysis and Applications 21 1 105 117 Springer Science and Business Media LLC 1433-7541 1433-755X EEG; Biometrics; Identification; Verification 1 2 2018 2018-02-01 10.1007/s10044-016-0569-4 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-12-30T14:35:22.7735187 2021-12-06T22:15:08.1183238 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Scott Yang 0000-0002-6618-7483 1 Farzin Deravi 2 Sanaul Hoque 3 58935__21970__c255f2df6c8241d6ad005a5b7fa064e4.pdf 58935.pdf 2021-12-30T14:33:36.9793147 Output 911176 application/pdf Version of Record true Copyright: The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Task sensitivity in EEG biometric recognition
spellingShingle Task sensitivity in EEG biometric recognition
Scott Yang
title_short Task sensitivity in EEG biometric recognition
title_full Task sensitivity in EEG biometric recognition
title_fullStr Task sensitivity in EEG biometric recognition
title_full_unstemmed Task sensitivity in EEG biometric recognition
title_sort Task sensitivity in EEG biometric recognition
author_id_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b
author_id_fullname_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b_***_Scott Yang
author Scott Yang
author2 Scott Yang
Farzin Deravi
Sanaul Hoque
format Journal article
container_title Pattern Analysis and Applications
container_volume 21
container_issue 1
container_start_page 105
publishDate 2018
institution Swansea University
issn 1433-7541
1433-755X
doi_str_mv 10.1007/s10044-016-0569-4
publisher Springer Science and Business Media LLC
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
active_str 0
description This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who performed four different motor movement/imagery tasks while their data were recorded. Training and test of the system was performed using a number of experimental protocols to establish if training with one type of task and tested with another would significantly affect the recognition performance. Also, experiments were conducted to evaluate the performance when a mixture of data from different tasks was used for training. The results suggest that performance is not significantly affected when there is a mismatch between training and test tasks. Furthermore, as the amount of data used for training is increased using a combination of data from several tasks, the performance can be improved. These results indicate that a more flexible approach may be incorporated in data collection for EEG-based biometric systems which could facilitate their deployment and improved performance.
published_date 2018-02-01T04:15:51Z
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score 11.012678

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