Brain Biometrics of Steady-State Visual Evoked Potential Functional Networks

Brain biometrics, due to its unique confidentiality and concealment, has received the increasing attention of scientific researchers in recent years. It has shown that steady-state visual evoked potential (SSVEP) signals with a high signal-to-noise ratio and stable spectrum can be used as identification features. However, current studies rely on the extraction of features characterizing the activity of single brain regions, ignoring the functional coupling between different brain regions. In this study, we proposed a novel approach that considers the functional connectivity of SSVEP signals collected by different electrodes as practical biometric features. We investigated individual coherence (COH) connectivity and phase synchronization according to different visual stimulations in terms of frequency component analysis of different principal bands. Fifteen subjects were identified by the support vector machine, random forest, and $k$ -nearest neighbor algorithm. The results show that distinctive features in SSVEP functional networks among individuals can achieve high precision identification (up to 98%). The obtained identification performance shows that SSVEP spectral COH and phase synchronization have the potential for biometric applications.