Abstract
The problem of identifying a person using biometric data is interesting. In this paper, the uniqueness of EEG signals of individuals is used to determine personal identity. EEG signals can be measured from different locations, but too many signals can degrade the recognition speed and accuracy. A practical technique combining Independent Component Analysis (ICA) for signal cleaning and a supervised neural network for classifying signals is proposed. From 16 EEG different signal locations, four truly relevant locations F 7, C 3, P 3, and O 1 were selected. This selection can identify a group of 20 persons with high accuracy. © 2010 Springer-Verlag Berlin Heidelberg.
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Tangkraingkij, P., Lursinsap, C., Sanguansintukul, S., & Desudchit, T. (2010). Personal identification by EEG using ICA and neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6018 LNCS, pp. 419–430). Springer Verlag. https://doi.org/10.1007/978-3-642-12179-1_35
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