| Autor: |
Christos A. Fidas, Dimitrios Lyras |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 22917-22934 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3253026 |
| Popis: |
Recently, the use of Electroencephalography (EEG) in scientific research on User Authentication (UA) has led to cutting-edge experiments that seek to identify and authenticate individuals based on their brain activity in particular usage scenarios. Utilizing EEG signals, derived from brain activity, might provide innovative solutions to contemporary security issues in traditional knowledge-based user authentication, including the threat of shoulder surfing. In this review paper, we analyze 108 different EEG-based user authentication experiments based on the following perspectives: a) the user experimental setup, with an emphasis on the applied EEG- protocols; b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad time frame from 1998 to 2022 and include various experimental protocols and algorithms used for classifying EEG signals. Additionally, the majority of the referenced works report findings from multiple experiments that incorporate distinct approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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