國立高雄大學圖資館 |

Language: English

Back

Brain fingerprint identification

Record Type:	Electronic resources : Monograph/item
Title/Author:	Brain fingerprint identificationby Wanzeng Kong, Xuanyu Jin.
Author:	Kong, Wanzeng.
other author:	Jin, Xuanyu.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xvii, 190 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-981-96-4512-1
ISBN:	9789819645121$q(electronic bk.)

Brain fingerprint identification
Kong, Wanzeng.

Brain fingerprint identification[electronic resource] /by Wanzeng Kong, Xuanyu Jin. - Singapore :Springer Nature Singapore :2025. - xvii, 190 p. :ill., digital ;24 cm. - Brain informatics and health,2367-1750. - Brain informatics and health..

Chapter 1 Overall of Brain Fingerprint Identification -- Chapter 2 Basics of EEG Signals -- Chapter 3 Multi-Task Brain Fingerprint Identification Based on Brain Networks -- Chapter 4 Multi-Task Brain Fingerprint Identification Based on Low-Rank and Sparse Decomposition Model -- Chapter 5 Multi-Task Brain Fingerprint Identification Based on Residual and Multi-scale Spatio-temporal Convolution Neural Network (RAMST-CNN) -- Chapter 6 Multi-Task Brain Fingerprint Identification Based on Convolutional Tensor-Train Neural Network (CTNN) -- Chapter 7 Specific-Task and Multi-Session Brain Fingerprint Identification Based on Multi-scale Convolution and Graph Pooling Network (MCGP) -- Chapter 8 Multi-Task and Multi-Session Brain Fingerprint Identification Based on Tensorized Spatial-Frequency Attention Network with Domain Adaptation (TSFAN) -- Chapter 9 Task-independent Cross-Session Brain Fingerprint Identification Based on Disentangled Adversarial Generalization Network (DAGN) -- Chapter 10 Summary.

Open Access

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.

ISBN: 9789819645121$q(electronic bk.)

Standard No.: 10.1007/978-981-96-4512-1doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q334

Dewey Class. No.: 006.3

Brain fingerprint identification
LDR:04232nmm a22003495a 4500 001 692771
003 DE-He213
005 20250806175214.0
006 m d
007 cr nn 008maaau
008 260622s2025 si s 0 eng d
020 $a 9789819645121$q(electronic bk.)
020 $a 9789819645114$q(paper)
024 7 $a 10.1007/978-981-96-4512-1 $2 doi
035 $a 978-981-96-4512-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q334
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q334 $b .K82 2025
100 1 $a Kong, Wanzeng. $3 1009199
245 1 0 $a Brain fingerprint identification $h [electronic resource] / $c by Wanzeng Kong, Xuanyu Jin.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xvii, 190 p. : $b ill., digital ; $c 24 cm.
490 1 $a Brain informatics and health, $x 2367-1750
505 0 $a Chapter 1 Overall of Brain Fingerprint Identification -- Chapter 2 Basics of EEG Signals -- Chapter 3 Multi-Task Brain Fingerprint Identification Based on Brain Networks -- Chapter 4 Multi-Task Brain Fingerprint Identification Based on Low-Rank and Sparse Decomposition Model -- Chapter 5 Multi-Task Brain Fingerprint Identification Based on Residual and Multi-scale Spatio-temporal Convolution Neural Network (RAMST-CNN) -- Chapter 6 Multi-Task Brain Fingerprint Identification Based on Convolutional Tensor-Train Neural Network (CTNN) -- Chapter 7 Specific-Task and Multi-Session Brain Fingerprint Identification Based on Multi-scale Convolution and Graph Pooling Network (MCGP) -- Chapter 8 Multi-Task and Multi-Session Brain Fingerprint Identification Based on Tensorized Spatial-Frequency Attention Network with Domain Adaptation (TSFAN) -- Chapter 9 Task-independent Cross-Session Brain Fingerprint Identification Based on Disentangled Adversarial Generalization Network (DAGN) -- Chapter 10 Summary.
506 0 $a Open Access
520 $a 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.
650 0 $a Artificial intelligence. $3 194058
650 0 $a Biometric identification. $3 256971
650 0 $a Human-machine systems. $3 206336
650 0 $a Machine learning. $3 188639
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Biometrics. $3 274525
650 2 4 $a Human-Machine Interfaces. $3 922364
650 2 4 $a Machine Learning. $3 833608
700 1 $a Jin, Xuanyu. $3 1009200
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Brain informatics and health. $3 820672
856 4 0 $u https://doi.org/10.1007/978-981-96-4512-1
950 $a Artificial Intelligence (R0) (SpringerNature-85269)