Authentication using c-VEP evoked in a mild-burdened cognitive task

Abstract

In recent years, more and more researchers are devoting themselves to the studies about authentication based on biomarkers. Among a wide variety of biomarkers, code-modulated visual evoked potential (c-VEP) has attracted increasing attention due to its significant role in the field of brain-computer interface. In this study, we designed a mild-burdened cognitive task (MBCT), which can check whether participants focus their attention on the visual stimuli that evoke c-VEP. Furthermore, we investigated the authentication based on the c-VEP evoked in the cognitive task by introducing a deep learning method. Seventeen participants were recruited to take part in the MBCT experiments including two sessions, which were carried out on two different days. The c-VEP signals from the first session were extracted to train the authentication deep models. The c-VEP data of the second session were used to verify the models. It achieved a desirable performance, with the average accuracy and F1 score, respectively, of 0.92 and 0.89. These results show that c-VEP carries individual discriminative characteristics and it is feasible to develop a practical authentication system based on c-VEP.

Burst c-VEP Based BCI: Optimizing Stimulus Design for Enhanced Classification with Minimal Calibration Data and Improved User Experience

“…For both types of c-VEP stimulation, a CNNbased decoding method was applied. This CNN-based decoding approach was demonstrated to yield superior performance in terms of classification accuracy and selection time for the decoding of msequence c-VEP than traditional template-matching methods (Nagel and Spüler, 2019b;Darmet et al, 2023;Huang et al, 2023;Li and Huang, 2021). Nonetheless, it is critical to consider alternative classification and decoding methods to explore solutions that will further enhance BCI performance.…”

“…This updated approach has exhibited remarkable performance, boasting exceptional Information Transfer Rates (ITR) along with unprecedentedly swift selection times in less than 2 s (Nagel and Spüler, 2019b;Li and Huang, 2021). Moreover, the CNN implementation to the approach offers interesting prospects for transfer learning (Huang et al, 2023).…”