AI authentication data_EEG

Traditional authentication models are vulnerable to security breaches when personal data is exposed. This study introduces novel hybrid visual stimuli protocols integrating event-related potentials (ERP) and steady-state visually evoked potentials (SSVEP) 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. Additionally, a 10Hz grow/shrink background image successfully elicited SSVEP. Furthermore, the comparison of harmonic and fundamental frequencies aids in optimizing neural interfaces.