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An evaluation of transfer learning models in EEG-based authentication
Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifac...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400490/ https://www.ncbi.nlm.nih.gov/pubmed/37535168 http://dx.doi.org/10.1186/s40708-023-00198-4 |
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author | Yap, Hui Yen Choo, Yun-Huoy Mohd Yusoh, Zeratul Izzah Khoh, Wee How |
author_facet | Yap, Hui Yen Choo, Yun-Huoy Mohd Yusoh, Zeratul Izzah Khoh, Wee How |
author_sort | Yap, Hui Yen |
collection | PubMed |
description | Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifacts. Therefore, further processing of data analysis is needed to retrieve useful information. Various machine learning approaches have been proposed and implemented in the EEG-based domain, with deep learning being the most current trend. However, retaining the performance of a deep learning model requires substantial computational effort and a vast amount of data, especially when the models go deeper to generate consistent results. Deep learning models trained with small data sets from scratch may experience an overfitting issue. Transfer learning becomes an alternative solution. It is a technique to recognize and apply the knowledge and skills learned from the previous tasks to a new domain with limited training data. This study attempts to explore the applicability of transferring various pre-trained models’ knowledge to the EEG-based authentication domain. A self-collected database that consists of 30 subjects was utilized in the analysis. The database enrolment is divided into two sessions, with each session producing two sets of EEG recording data. The frequency spectrums of the preprocessed EEG signals are extracted and fed into the pre-trained models as the input data. Three experimental tests are carried out and the best performance is reported with accuracy in the range of 99.1–99.9%. The acquired results demonstrate the efficiency of transfer learning in authenticating an individual in this domain. |
format | Online Article Text |
id | pubmed-10400490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-104004902023-08-05 An evaluation of transfer learning models in EEG-based authentication Yap, Hui Yen Choo, Yun-Huoy Mohd Yusoh, Zeratul Izzah Khoh, Wee How Brain Inform Research Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifacts. Therefore, further processing of data analysis is needed to retrieve useful information. Various machine learning approaches have been proposed and implemented in the EEG-based domain, with deep learning being the most current trend. However, retaining the performance of a deep learning model requires substantial computational effort and a vast amount of data, especially when the models go deeper to generate consistent results. Deep learning models trained with small data sets from scratch may experience an overfitting issue. Transfer learning becomes an alternative solution. It is a technique to recognize and apply the knowledge and skills learned from the previous tasks to a new domain with limited training data. This study attempts to explore the applicability of transferring various pre-trained models’ knowledge to the EEG-based authentication domain. A self-collected database that consists of 30 subjects was utilized in the analysis. The database enrolment is divided into two sessions, with each session producing two sets of EEG recording data. The frequency spectrums of the preprocessed EEG signals are extracted and fed into the pre-trained models as the input data. Three experimental tests are carried out and the best performance is reported with accuracy in the range of 99.1–99.9%. The acquired results demonstrate the efficiency of transfer learning in authenticating an individual in this domain. Springer Berlin Heidelberg 2023-08-03 /pmc/articles/PMC10400490/ /pubmed/37535168 http://dx.doi.org/10.1186/s40708-023-00198-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Yap, Hui Yen Choo, Yun-Huoy Mohd Yusoh, Zeratul Izzah Khoh, Wee How An evaluation of transfer learning models in EEG-based authentication |
title | An evaluation of transfer learning models in EEG-based authentication |
title_full | An evaluation of transfer learning models in EEG-based authentication |
title_fullStr | An evaluation of transfer learning models in EEG-based authentication |
title_full_unstemmed | An evaluation of transfer learning models in EEG-based authentication |
title_short | An evaluation of transfer learning models in EEG-based authentication |
title_sort | evaluation of transfer learning models in eeg-based authentication |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400490/ https://www.ncbi.nlm.nih.gov/pubmed/37535168 http://dx.doi.org/10.1186/s40708-023-00198-4 |
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