AI-powered computer interface using evoked potentials for XR biometric authentication and individual neural profiling

Abstract

Most authentication models are vulnerable to security breaches when personal data is exposed. This study introduces a novel hybrid visual computer interface integrating event-related potentials (ERPs) and steady-state visually evoked potentials (SSVEPs) to develop an authentication system that enhances both performance and personalization in neural interfaces. Our model utilizes distinctive neural patterns elicited by a range of visual stimuli based on 4-digit numbers, such as familiar numbers (personal birthdates, excluding targets), standard targets, and non-targets. The results revealed a distinct P300 response to familiar numbers when compared to both non-target and target stimuli. Incorporating these stimuli into our Transformer-based authentication system, coupled with personalized electroencephalogram (EEG) data segmentation, resulted in high accuracy in authenticating users and demonstrated remarkable robustness against security breaches. Additionally, a 10 Hz grow/shrink background image successfully elicited SSVEP. Furthermore, the comparison of harmonic and fundamental frequencies aids in optimizing neural interfaces.