Brain Password

Lin, Feng; Cho, Kun Woo; Song, Chen; Xu, Wenyao; Jin, Zhanpeng

doi:10.1145/3210240.3210344

Cited by 38 publications

(2 citation statements)

References 44 publications

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“…Chuang et al (2013) [13], Gopal et al (2021)[25], andLiew et al (2018) [44] opted for two different session recordings, Armstrong et al(2015) [5], [46] implemented three different recording sessions.EEG-Montage. External-stimuli protocols mainly use O1, O2 and Oz channels located in the back region, from which high frequencies related to the visual context comprehension are utilized.…”

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confidence: 99%

“…The analyzed literature suggests the following methods: Gui et al[26], Kaur et al[35] and Chen et al[12] employed Wavelet transformation, Debie et al[18], whereas Vahid et al[68] and Pham et al[54] opted for PSD (Power Spectral Density). Lin et al[46] and Pham et al[54] used AR models in their work and Gopal et al[25] preferred CFS (Correlation based Feature Selection). Finally, Ozdenici et al[80] opted for multi-modal feature extracting approaches.With respect to the algorithms used for the EEG classification task, the most common technique employed was the SVMs, as in the works of: Wiliaiprasitporn[72], Wang et al [71], Sooriyaarachchi et al [65], Lee et al [42], Armstrong et al [5], Arnau Gonzalez et al [6], Debie et al [18], Lin et al [46], Chen et al [12], Kaur et al [35], Chen et al [12], Vahid et al [68], Alzahab et al [112], Rahman et al [114], Leon et al[117], and Gupta et al [28].…”

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confidence: 99%

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A Review of EEG-Based User Authentication: Trends and Future Research Directions

Fidas

Lyras²

2023

IEEE Access

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The employment of Electroencephalography (EEG) in the User Authentication (UA) scientific research has recently unlocked state-of-the-art experimentation, aiming at identifying and authenticating individuals given their brainiac activity within specific contexts of use. Indeed, utilizing EEG signals that are derived from brainiac activities can be used for tackling existing UA security threats such as shoulder surfing, thus providing a novel solution to contemporary security problems in traditional knowledge-based user authentication. In this survey, we aim to complete previous literature surveys by providing a systematic classification and presentation of existing research that is based on the following pillars: a) the user experimental setup, with an emphasis on the applied EEG-acquisition protocols (e.g., rest, external stimuli driven, mental and hybrid); b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad area of experiment protocols and various algorithms used for EEG signal classification. Moreover, most cited works include results from more than one experiments with different 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.

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confidence: 99%