EEG Fingerprints: Phase Synchronization of EEG Signals as Biomarker for Subject Identification

The goal of biometrics is to recognize humans based on their physical and behavioral characteristics. Preliminary studies have demonstrated that the electroencephalogram(EEG) is potentially more secure and private than traditional biometric identifiers. At present, the EEG identification method targets specific tasks and cannot be generalized. In this study, a novel EEG-based biometric identification method that extracts the phase synchronization (PS) features for subject identification is proposed under a variety of tasks. We quantified the PS features by the phase locking value (PLV) in different frequency bands. Subsequently, we employed the principal component analysis (PCA) to reduce the dimension. Then, we used the linear discriminant analysis (LDA) to construct a projection space and projected the features onto the projection space. Finally, a feature vector was assigned to the class label. The experimental results of the proposed method used on 3 datasets with different cognitive tasks showed high classification accuracies and relatively good stabilities. From the results, we found that particularly in the beta and gamma bands, the average accuracies are more than 97% with the standard deviation equal to or less than the magnitude 10e-2 for both Dataset 1 and Dataset 2. For Dataset 3, the PS feature vectors in all off the bands have high classification accuracies, which are more than 97% with the standard deviation of the same magnitude. Our work demonstrated that the phase synchronization of EEG signals has task-free biometric properties, which can be used for subject identification.