Using EEG to Predict and Analyze Password Memorability

Alomari, Ruba; Martin, Miguel Vargas; MacDonald, Shane; Bellman, Christopher

doi:10.1109/iccc.2019.00019

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…Feature extraction, Classification and Accuracy. Regarding the most commonly used feature extraction schemes, the analyzed literature suggests the following: Alomari et al [4] and Kumarisharma et al [64] employed Wavelet transformations whereas Debie et al [18] and Marcel et al [52] opted for PSD (Power Spectral Density). Finally, Valsaraj et al [69], Pham et al [54] and Haukipuro et al [30] opted for multi-modal feature extracting approaches.…”

Section: Mental Activity Protocolsmentioning

confidence: 99%

“…Concerning the algorithms selected for classification of the EEG data, the most commonly applied technique was the Support Vector Machines (SVM) as appearing in following works: Alomari et al [4], DaSalla et al [16], Pham et al [54]. Moreover, Convolutional Neural Networks (CNNs) were applied in Das et al [15], Sun et al [66] whereas Self/Cross Similarities was applied in Chuang et al [13] and Genetic Algorithms in Lim et al [45].…”

Section: Mental Activity Protocolsmentioning

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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