Jeong Siwoo Ko Jonghyeon Park Sangin Ha, Jihyeon Chae Min Seong Kim, Lae hyun Mun Sungchul 2026-04-08T09:30:07Z 2026-04-08T09:30:07Z 2026-04-07 2026-04 0208-5216 https://pubs.kist.re.kr/handle/201004/154547 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. English Polish Scientific Publishers PWN AI-powered computer interface using evoked potentials for XR biometric authentication and individual neural profiling Article 10.1016/j.bbe.2026.03.005 1 Biocybernetics and Biomedical Engineering, v.46, no.2, pp.365 - 381 Biocybernetics and Biomedical Engineering 46 2 365 381 N scie scopus 001730494800001