Electroencephalographic feature evaluation for improving personal authentication performance

Electroencephalography (EEG), a method of continuously recording the electrical activity of the brain, provides signals that are among the most promising types of information usable in vital biometrics. However, reliable biometrics based on EEG are still under development since it remains unclear how to extract EEG features that can be used to identify individuals the most effectively. In this study, new EEG features for use in biometrics were proposed and their effectiveness for personal authentication was demonstrated using an open-access EEG database containing 109 personal EEG datasets. From the EEG signals, we extracted 10 single-channel features (seven spectral and three nonlinear) by performing spectral and nonlinear analyses and 10 multichannel features by conducting network analysis based on phase synchronization. A distance-based classifier was built based on the extracted features to distinguish the self from the others. The performance of the proposed personal authentication scheme was assessed in terms of the equal error rate (EER) and false rejection rate (FRR) when the false acceptance rate (FAR) was fixed at 1%. The EER was 0.73% with the eyes open (REO) and 1.80% with the eyes closed (REC), and the FRR with a 1% FAR was 1.10% (REO) and 2.20% (REC). These results are superior to those of previous studies in which the same database was used. In addition, the nonlinear and network features appeared more important than the spectral features for authentication. This method of utilizing EEG features for personal authentication is expected to facilitate the advancement of EEG-based biometric systems.