Emerging biometrics: EEG-based identity verification

We show that the electroencephalogram (EEG) contains individual-specific information, which can be extracted and used for identity verification. We applied both linear (Auto Regressive) and nonlinear (Generalized Autoregressive Conditional Heteroskedasticity) models to 16 second frames of one channel EEG down-sampled to frequency 60 Hz. We used parameters of this model as the features and employed a Mahalanobis distance-based classifier for identity 88% of correct verifications, showing the potential of our approach, as well as indicating that the results are time invariant.