Brain Fingerprint Identification
This open access book delves into the emerging field of biometric identification using brainwave patterns. Specifically, this book presents recent advances in electroencephalography (EEG)-based biometric recognition to identify unique neural signatures that can be used for secure authentication and...
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| Main Authors: | , |
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| 格式: | Online |
| 語言: | 英语 |
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Springer Nature
2025
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| 主題: | |
| 在線閱讀: | ONIX_20250613T105552_9789819645121_37 |
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| _version_ | 1869513794368045056 |
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| author | Kong, Wanzeng Jin, Xuanyu |
| author_browse | Jin, Xuanyu Kong, Wanzeng |
| author_facet | Kong, Wanzeng Jin, Xuanyu |
| author_sort | Kong, Wanzeng |
| collection | Directory of Open Access Books |
| description | This open access book delves into the emerging field of biometric identification using brainwave patterns. Specifically, this book presents recent advances in electroencephalography (EEG)-based biometric recognition to identify unique neural signatures that can be used for secure authentication and identification. Traditional biometric systems such as fingerprints, iris scans, and face recognition have become integral to security and identification. However, these methods are increasingly vulnerable to spoofing and other forms of attack. Unlike other traditional biometrics, EEG signals are non-invasive, continuous authentication, liveness detection, and resistance to coercion due to the complexity and uniqueness of brain patterns. Therefore, it is particularly suitable for high-security fields such as military and finance, providing a promising alternative for future high-security identification and authentication. However, most of the existing brain fingerprint identification studies require subjects to perform specific cognitive tasks, which limits the popularization and application of brain fingerprint identification in practical scenarios. Additionally, due to the low signal-to-noise ratio (SNR) and time-varying characteristics of EEG signals, there are distribution differences in EEG data across sessions from several days, leading to stability issues in brain fingerprint features extracted at different sessions. Finally, because the EEG signal is affected by the coupling of multiple factors and the nervous system has continuous spontaneous variability, which makes it difficult for the brain fingerprint identification model to be suitable for the scenarios of unseen sessions and cognitive tasks, and there is the problem of insufficient model generalization. In this book, based on traditional machine learning methods and deep learning methods, the authors will carry out multi-task single-session, single-task multi-session, and multi-task multi-session brain fingerprint identification research respectively for the above problems, to provide an effective solution for the application of brain fingerprint identification in practical scenarios. |
| format | Online |
| id | doab-20.500.12854ir-161334 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1613342025-06-14T05:03:07Z Brain Fingerprint Identification Kong, Wanzeng Jin, Xuanyu Brain-Computer Interface EEG signal Biometrics, Security Brain Network Low-Rank and Sparse Decomposition Residual Network Graph Neural Network Tensorial Neural Networks Domain Adaptation Domain Generalization thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQP Pattern recognition thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning This open access book delves into the emerging field of biometric identification using brainwave patterns. Specifically, this book presents recent advances in electroencephalography (EEG)-based biometric recognition to identify unique neural signatures that can be used for secure authentication and identification. Traditional biometric systems such as fingerprints, iris scans, and face recognition have become integral to security and identification. However, these methods are increasingly vulnerable to spoofing and other forms of attack. Unlike other traditional biometrics, EEG signals are non-invasive, continuous authentication, liveness detection, and resistance to coercion due to the complexity and uniqueness of brain patterns. Therefore, it is particularly suitable for high-security fields such as military and finance, providing a promising alternative for future high-security identification and authentication. However, most of the existing brain fingerprint identification studies require subjects to perform specific cognitive tasks, which limits the popularization and application of brain fingerprint identification in practical scenarios. Additionally, due to the low signal-to-noise ratio (SNR) and time-varying characteristics of EEG signals, there are distribution differences in EEG data across sessions from several days, leading to stability issues in brain fingerprint features extracted at different sessions. Finally, because the EEG signal is affected by the coupling of multiple factors and the nervous system has continuous spontaneous variability, which makes it difficult for the brain fingerprint identification model to be suitable for the scenarios of unseen sessions and cognitive tasks, and there is the problem of insufficient model generalization. In this book, based on traditional machine learning methods and deep learning methods, the authors will carry out multi-task single-session, single-task multi-session, and multi-task multi-session brain fingerprint identification research respectively for the above problems, to provide an effective solution for the application of brain fingerprint identification in practical scenarios. 2025-06-14T05:03:07Z 2025-06-14T05:03:07Z 2025-06-13T09:21:17Z 2025 book ONIX_20250613T105552_9789819645121_37 https://library.oapen.org/handle/20.500.12657/103593 9789819645121 9789819645114 https://directory.doabooks.org/handle/20.500.12854/161334 eng Brain Informatics and Health open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/103593/1/9789819645121.pdf Springer Nature Springer Nature Singapore 10.1007/978-981-96-4512-1 10.1007/978-981-96-4512-1 9fa3421d-f917-4153-b9ab-fc337c396b5a 219cc0eb-31a9-46a1-a50f-c2d756c7fec1 9cdf5cb9-2405-4a80-ab0e-bce2dfd5e63e 9789819645121 9789819645114 Springer Nature Singapore 190 Singapore [...] National Natural Science Foundation of China Chinese National Science Foundation 10.13039/501100001809 open access |
| spellingShingle | Brain-Computer Interface EEG signal Biometrics, Security Brain Network Low-Rank and Sparse Decomposition Residual Network Graph Neural Network Tensorial Neural Networks Domain Adaptation Domain Generalization thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQP Pattern recognition thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Kong, Wanzeng Jin, Xuanyu Brain Fingerprint Identification |
| title | Brain Fingerprint Identification |
| title_full | Brain Fingerprint Identification |
| title_fullStr | Brain Fingerprint Identification |
| title_full_unstemmed | Brain Fingerprint Identification |
| title_short | Brain Fingerprint Identification |
| title_sort | brain fingerprint identification |
| topic | Brain-Computer Interface EEG signal Biometrics, Security Brain Network Low-Rank and Sparse Decomposition Residual Network Graph Neural Network Tensorial Neural Networks Domain Adaptation Domain Generalization thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQP Pattern recognition thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Brain-Computer Interface EEG signal Biometrics, Security Brain Network Low-Rank and Sparse Decomposition Residual Network Graph Neural Network Tensorial Neural Networks Domain Adaptation Domain Generalization thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQP Pattern recognition thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20250613T105552_9789819645121_37 |
| work_keys_str_mv | AT kongwanzeng brainfingerprintidentification AT jinxuanyu brainfingerprintidentification |