This study investigates the use of electroencephalography (EEG) signals for user authentication as an innovative approach to enhancing security within the cybersecurity domain. Motivated by the limitations of traditional authentication mechanisms, we explore the viability of brainwave patterns as distinctive biometric markers for verifying user identity. This research utilizes a publicly available EEG authentication dataset comprising recordings from 38 participants, with data elicited through paradigms designed to evoke P300 and N400 event-related potentials (ERPs). A rigorous methodological framework was employed, including signal preprocessing, ERP and power spectral density (PSD) feature extraction, and a comparative evaluation of multiple machine learning and deep learning classifiers, such as support vector machines (SVMs), random forests (RFs), and convolutional neural networks (CNNs). The proposed CNN model demonstrated superior performance, achieving 99% accuracy in the N400-Faces task, highlighting its effectiveness in discerning complex neural signatures associated with semantic and facial stimuli. The findings of this study substantiate the feasibility of EEG-based biometrics as a secure, noninvasive authentication modality and contribute to the advancement of resilient authentication frameworks.
Al-Nafjan et al. (Mon,) studied this question.
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