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The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the Visual Evoked Potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis.
Biometrics, EEG gamma band, Elman neural network, MUSIC, k--nearest neighbors, visual evoked potential.

D. P. Mandic and R. Palaniappan, "Biometrics from Brain Electrical Activity: A Machine Learning Approach," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 738-742, 2007.
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