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

Wanzeng Kong,Sijia Xu,Fabio Babiloni,Hang Chen,Luyun Wang

doi:10.1109/access.2019.2931624

Abstract

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.

Highlights

The electroencephalogram (EEG) records the electrical activity of the human brain along the scalp and reflects the summation of the synchronous activity of thousands or millions of neurons that have a similar spatial orientation [1], [2]
The phase locking values in the proposed method are computed from the EEG signals with a one-second time window
To get more effective phase synchronization (PS) features, we compare the classification results in the alpha band with four different time lengths, and the average phase locking value (PLV) matrices that are calculated from 30 s EEG signals produce better results

Summary

Introduction

The electroencephalogram (EEG) records the electrical activity of the human brain along the scalp and reflects the summation of the synchronous activity of thousands or millions of neurons that have a similar spatial orientation [1], [2]. The scalp EEG activity shows oscillations at a variety of frequencies Several of these oscillations have characteristic frequency ranges and spatial distributions, which are associated with different states of brain functioning. EEG-based recognition systems are robust and secure against spoofing identification at the sensor by attackers because attackers cannot covertly acquire EEG signals in their physical form or synthetically generate them at a later time. Another advantage of EEG-based biometric systems is that they are available for people with certain physical disabilities or injuries.

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