Identifying individuals using EEG-Based brain connectivity patterns

Considering the recent rapid advancements in digital technology, electroencephalogram (EEG) signal is a potential candidate for a robust human biometric authentication system. In this paper the focus of investigation is the use of brain activity as a new modality for identification. Univariate mod...

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Main Author: Zubaidi, A.L.A.
Format: Conference or Workshop Item
Language:English
Published: 2021
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Online Access:http://eprints.unisza.edu.my/4147/1/FH03-FP-21-56622.pdf
http://eprints.unisza.edu.my/4147/
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spelling my-unisza-ir.41472021-12-28T06:19:20Z http://eprints.unisza.edu.my/4147/ Identifying individuals using EEG-Based brain connectivity patterns Zubaidi, A.L.A. R Medicine (General) RZ Other systems of medicine Considering the recent rapid advancements in digital technology, electroencephalogram (EEG) signal is a potential candidate for a robust human biometric authentication system. In this paper the focus of investigation is the use of brain activity as a new modality for identification. Univariate model biometrics such as speech, heart sound and electrocardiogram (ECG) require high-resolution computer system with special devices. The heart sound is obtained by placing the digital stethoscope on the chest, the ECG signals at the hands or chest of the client and speaks into a microphone for speaker recognition. It is challenging task when adapting these technologies to human beings. This paper proposed a series of tasks in a single paradigm rather than having users perform several tasks one by one. The advantage of using brain electrical activity as suggested in this work is its uniqueness; the recorded brain response cannot be duplicated, and a person’s identity is therefore unlikely to be forged or stolen. The disadvantage of applying univariate is that the process only includes correlation in time precedence of a signal, while the correlation between regions is ignored. The inter-regional could not be assessed directly from univariate models. The alternative to this problem is the generalization of univariate model to multivariate modeling, hypothesized that the inter-regional correlations could give additional information to discriminate between brain conditions where the models or methods can measure the synchronization between coupling regions and the coherency among them on brain biometrics. The key issue is to handle the single task paradigm proposed in this paper with multivariate signal EEG classification using Multivariate Autoregressive (MVAR) rather than univariate model. The brain biometric systems obtained a significant result of 95.33% for dynamic Vector autoregressive (VAR) time series and 94.59% for Partial Directed Coherence (PDC) and Coherence (COH) frequency domain features. © 2021, Springer Nature Switzerland AG. 2021 Conference or Workshop Item NonPeerReviewed text en http://eprints.unisza.edu.my/4147/1/FH03-FP-21-56622.pdf Zubaidi, A.L.A. (2021) Identifying individuals using EEG-Based brain connectivity patterns. In: 14th International Conference on Brain Informatics, 17-19 Sep 2021, Virtual, Online.
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic R Medicine (General)
RZ Other systems of medicine
spellingShingle R Medicine (General)
RZ Other systems of medicine
Zubaidi, A.L.A.
Identifying individuals using EEG-Based brain connectivity patterns
description Considering the recent rapid advancements in digital technology, electroencephalogram (EEG) signal is a potential candidate for a robust human biometric authentication system. In this paper the focus of investigation is the use of brain activity as a new modality for identification. Univariate model biometrics such as speech, heart sound and electrocardiogram (ECG) require high-resolution computer system with special devices. The heart sound is obtained by placing the digital stethoscope on the chest, the ECG signals at the hands or chest of the client and speaks into a microphone for speaker recognition. It is challenging task when adapting these technologies to human beings. This paper proposed a series of tasks in a single paradigm rather than having users perform several tasks one by one. The advantage of using brain electrical activity as suggested in this work is its uniqueness; the recorded brain response cannot be duplicated, and a person’s identity is therefore unlikely to be forged or stolen. The disadvantage of applying univariate is that the process only includes correlation in time precedence of a signal, while the correlation between regions is ignored. The inter-regional could not be assessed directly from univariate models. The alternative to this problem is the generalization of univariate model to multivariate modeling, hypothesized that the inter-regional correlations could give additional information to discriminate between brain conditions where the models or methods can measure the synchronization between coupling regions and the coherency among them on brain biometrics. The key issue is to handle the single task paradigm proposed in this paper with multivariate signal EEG classification using Multivariate Autoregressive (MVAR) rather than univariate model. The brain biometric systems obtained a significant result of 95.33% for dynamic Vector autoregressive (VAR) time series and 94.59% for Partial Directed Coherence (PDC) and Coherence (COH) frequency domain features. © 2021, Springer Nature Switzerland AG.
format Conference or Workshop Item
author Zubaidi, A.L.A.
author_facet Zubaidi, A.L.A.
author_sort Zubaidi, A.L.A.
title Identifying individuals using EEG-Based brain connectivity patterns
title_short Identifying individuals using EEG-Based brain connectivity patterns
title_full Identifying individuals using EEG-Based brain connectivity patterns
title_fullStr Identifying individuals using EEG-Based brain connectivity patterns
title_full_unstemmed Identifying individuals using EEG-Based brain connectivity patterns
title_sort identifying individuals using eeg-based brain connectivity patterns
publishDate 2021
url http://eprints.unisza.edu.my/4147/1/FH03-FP-21-56622.pdf
http://eprints.unisza.edu.my/4147/
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