Armstrong, B. C., Ruiz-Blondet, M., Khalifian, N., Jin, Zanpeng, J., Kurtz, K. J., Laszlo, S. (2015). Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics.  Neurocomputing, 166, 59-67.


Author's self-archived version (.pdf)  (42 pages)

Official version in Neurocomputing [external link]


The human brain continually generates electrical potentials representing neural communication.  These potentials can be measured at the scalp, and constitute the electroencephalogram (EEG).  When the EEG is time-locked to stimulation-- such as the presentation of a word--, and averaged over many such presentations, the Event-Related Potential (ERP) is obtained.  The functional characteristics of components of the ERP are well understood, and some components represent processing that may differ uniquely from individual to individual--such as the N400 component, which represents access to the semantic network. We applied several pattern classifiers to ERPs representing the response of individuals to a stream of text designed to be idiosyncratically familiar to different individuals.  Results indicate that there are robustly identifiable features of the ERP that enable labeling of ERPs as belonging to individuals with accuracy reliably above chance (in the range of 82-97%).  Further, these features are stable over time, as indicated by continued accurate identification of individuals from ERPs after a lag of up to six months.  Even better, the high degree of labeling accuracy achieved in all cases was achieved with the use of only 3 electrodes on the scalp-- the minimal possible number that can acquire clean data.

Keywords: Biometrics; EEG; Event-Related Potentials (ERPs); Pattern classification

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