Combing Multiple Visual Stimuli to Enhance the Performance of VEP-Based Biometrics

In recent years, electroencephalography (EEG) has received increasing attention in the field of biometrics because of its unique advantages such as covertness, resistance to spoofing, sensitivity to emotional and mental states, and continuous nature. Visual evoked potentials (VEPs) have been widely used in EEG-based biometrics owing to fast recognition speed and high accuracy. This study proposes a new method to combine multiple visual stimuli for VEP-based individual identification. Correct recognition rate (CRR) was estimated using steady-state VEPs (ss-VEPs), and code modulated VEPs (c-VEPs) recorded from a group of 35 subjects. c-VEPs achieved a 100% CRR using 3.1-s of VEP data (a 10.8-s duration, including 7.7-s intervals) in the cross-session condition. An online system based on the combination of stimuli optimized from the data of 35 subjects was further developed and validated with an additional group of 22 subjects. A cross-session CRR of 99.55% was achieved using the same parameters. These results indicate that the proposed VEP-based individual identification method using multiple visual stimuli shows great potential for practical applications.