LEADER 01250nam a2200349 i 4500 001 991000913809707536 005 20020507175740.0 008 960618s1967 us ||| | eng 020 $a0674324498 035 $ab10774968-39ule_inst 035 $aLE01304233$9ExL 040 $aDip.to Matematica$beng 082 0 $a511.3 084 $aAMS 00B60 084 $aAMS 01A05 084 $aAMS 01A55 084 $aAMS 01A60 084 $aAMS 03-03 084 $aAMS 03-XX 100 1 $aHeijenoort, Jean van$047663 245 10$aFrom Frege to Godel :$ba source book in Mathematical Logic, 1879-1931 /$cJean van Heijenoort 260 $aCambridge, MA :$bHarvard Univ. Press,$c1967 300 $aviii, 664 p. ;$c25 cm. 650 4$aHistory of mathematics 650 4$aMathematical logic-history 907 $a.b10774968$b21-09-06$c28-06-02 912 $a991000913809707536 945 $aLE013 01A HEI11 C.2 (1967)$g2$i2013000051307$lle013$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10873764$z28-06-02 945 $aLE013 01A HEI11 C.1 (1967)$g1$i2013000079394$lle013$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10873776$z28-06-02 996 $aFrom Frege to Godel$9336402 997 $aUNISALENTO 998 $ale013$b01-01-96$cm$da $e-$feng$gus $h0$i2 LEADER 05074nam 22006375 450 001 9911007350603321 005 20250604125746.0 010 $a981-9645-12-3 024 7 $a10.1007/978-981-96-4512-1 035 $a(CKB)39196810700041 035 $a(MiAaPQ)EBC32154192 035 $a(Au-PeEL)EBL32154192 035 $a(DE-He213)978-981-96-4512-1 035 $a(OCoLC)1527730199 035 $a(EXLCZ)9939196810700041 100 $a20250604d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBrain Fingerprint Identification /$fby Wanzeng Kong, Xuanyu Jin 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (293 pages) 225 1 $aBrain Informatics and Health,$x2367-1750 311 08$a981-9645-11-5 327 $aChapter 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. 330 $aThis 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. 410 0$aBrain Informatics and Health,$x2367-1750 606 $aArtificial intelligence 606 $aBiometric identification 606 $aHuman-machine systems 606 $aMachine learning 606 $aArtificial Intelligence 606 $aBiometrics 606 $aHuman-Machine Interfaces 606 $aMachine Learning 615 0$aArtificial intelligence. 615 0$aBiometric identification. 615 0$aHuman-machine systems. 615 0$aMachine learning. 615 14$aArtificial Intelligence. 615 24$aBiometrics. 615 24$aHuman-Machine Interfaces. 615 24$aMachine Learning. 676 $a006.3 700 $aKong$b Wanzeng$01826330 701 $aJin$b Xuanyu$01826331 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007350603321 996 $aBrain Fingerprint Identification$94394303 997 $aUNINA