Biometrics from Brain Electrical Activity: A Machine Learning Approach

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 4; pp. 738 - 742
• Main Authors: Palaniappan, R., Mandic, D.P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biometrics; Biometry - methods; Brain; Brain - physiology; Brain Mapping - methods; Brain modeling; Classification; EEG gamma band; Electric potential; Electroencephalography; Electroencephalography - methods; Elman neural network; Evoked Potentials - physiology; Fingerprint recognition; Forgery; Humans; Image storage; Intelligence; Iris; k > nearest neighbors; Machine learning; MUSIC; Pattern analysis; Pattern Recognition, Automated - methods; Simulation; Spatial databases; Visual; visual evoked potential
• ISSN: 0162-8828; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2007.1013

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