Cancellable Deep Learning Framework for EEG Biometrics-Reference-Cited by-同舟云学术

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Cancellable Deep Learning Framework for EEG Biometrics

Published:2024 Issue: Volume:19 Page:3745-3757
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Wang Min¹^ORCID,Yin Xuefei²^ORCID,Hu Jiankun³^ORCID

Affiliation:

1. School of Information Technology and Systems, University of Canberra, Canberra, ACT, Australia

2. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

3. School of Systems and Computing, University of New South Wales, Canberra, ACT, Australia

Funder

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10443934.pdf?arnumber=10443934

Reference40 articles.

1. Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

2. A Survey on Brain Biometrics

3. CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification

4. Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

5. BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs

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