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The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG

An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state...

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Autores principales: Di, Yang, An, Xingwei, Zhong, Wenxiao, Liu, Shuang, Ming, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8475761/
https://www.ncbi.nlm.nih.gov/pubmed/34588964
http://dx.doi.org/10.3389/fnhum.2021.672946
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author Di, Yang
An, Xingwei
Zhong, Wenxiao
Liu, Shuang
Ming, Dong
author_facet Di, Yang
An, Xingwei
Zhong, Wenxiao
Liu, Shuang
Ming, Dong
author_sort Di, Yang
collection PubMed
description An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85–100% for intra-experiment dataset, and were 80–100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.
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spelling pubmed-84757612021-09-28 The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG Di, Yang An, Xingwei Zhong, Wenxiao Liu, Shuang Ming, Dong Front Hum Neurosci Neuroscience An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85–100% for intra-experiment dataset, and were 80–100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system. Frontiers Media S.A. 2021-09-13 /pmc/articles/PMC8475761/ /pubmed/34588964 http://dx.doi.org/10.3389/fnhum.2021.672946 Text en Copyright © 2021 Di, An, Zhong, Liu and Ming. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Di, Yang
An, Xingwei
Zhong, Wenxiao
Liu, Shuang
Ming, Dong
The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
title The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
title_full The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
title_fullStr The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
title_full_unstemmed The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
title_short The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
title_sort time-robustness analysis of individual identification based on resting-state eeg
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8475761/
https://www.ncbi.nlm.nih.gov/pubmed/34588964
http://dx.doi.org/10.3389/fnhum.2021.672946
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