Staff View: EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules increment...

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Main Authors:	Liew, Siaw Hong, Choo, Yun Huoy, Yin, Fen Low, Mohd Yusoh, Zeratul Izzah
Format:	Article
Language:	English
Published:	The Institution Of Engineering And Technology (IET) 2018
Subjects:	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utem.edu.my/id/eprint/21658/2/BMT-2017-0044-FINAL.pdf http://eprints.utem.edu.my/id/eprint/21658/ http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0044
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id	my.utem.eprints.21658
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spelling	my.utem.eprints.216582021-08-16T21:17:11Z http://eprints.utem.edu.my/id/eprint/21658/ EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique Liew, Siaw Hong Choo, Yun Huoy Yin, Fen Low Mohd Yusoh, Zeratul Izzah T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules incrementally to maintain all representative electroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granules through insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reduce the overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processing steps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. The experimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNN technique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured in terms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. The proposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window size environment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model. The Institution Of Engineering And Technology (IET) 2018 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/21658/2/BMT-2017-0044-FINAL.pdf Liew, Siaw Hong and Choo, Yun Huoy and Yin, Fen Low and Mohd Yusoh, Zeratul Izzah (2018) EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique. IET Biometrics, 7 (2). pp. 145-152. ISSN 2047-4938 http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0044 10.1049/iet-bmt.2017.0044
institution	Universiti Teknikal Malaysia Melaka
building	UTEM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknikal Malaysia Melaka
content_source	UTEM Institutional Repository
url_provider	http://eprints.utem.edu.my/
language	English
topic	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Liew, Siaw Hong Choo, Yun Huoy Yin, Fen Low Mohd Yusoh, Zeratul Izzah EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique
description	This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules incrementally to maintain all representative electroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granules through insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reduce the overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processing steps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. The experimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNN technique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured in terms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. The proposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window size environment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model.
format	Article
author	Liew, Siaw Hong Choo, Yun Huoy Yin, Fen Low Mohd Yusoh, Zeratul Izzah
author_facet	Liew, Siaw Hong Choo, Yun Huoy Yin, Fen Low Mohd Yusoh, Zeratul Izzah
author_sort	Liew, Siaw Hong
title	EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique
title_short	EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique
title_full	EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique
title_fullStr	EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique
title_full_unstemmed	EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique
title_sort	eeg-based biometric authentication modelling using incremental fuzzy-rough nearest neighbour technique
publisher	The Institution Of Engineering And Technology (IET)
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/21658/2/BMT-2017-0044-FINAL.pdf http://eprints.utem.edu.my/id/eprint/21658/ http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0044
_version_	1709671883254792192
score	13.211869

EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

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