Biometrics from brain electrical activity: a machine learning approach.

Full Abstract

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.

Author information

Author/s: Palaniappan, Ramaswamy (R); Mandic, Danilo P (DP);

Affiliation: Department of Computer Science, University of Essex, Colchester, Wivenhoe Park, UK. rpalan(-atsign-)essex.ac.uk

Journal and publication information

Publication Type: Evaluation Studies; Journal Article

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2007-Apr; vol 29 (issue 4) : pp 738-42

Dates: Created 2007/02/14; Completed 2007/04/24;

PMID: 17299228, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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