This article addresses the main problems in person authentication using the EEG. This area is currently under active development due to advances in virtual spaces and seeking new methods for user recognition in different internet platforms. One task that needs to be solved is that of identifying stable EEG measures and patterns which might be used to perform reliable recognition of people over long time intervals. The second question considered here is that of selecting tasks for EEG recording protocols. In the present study, subjects’ EEG traces were recorded at rest and on performance of various motor tasks five times over three months and the stability of the different patterns was then compared. The results showed that the most stable was the α-rhythm pattern in the resting state with the eyes closed, with minimal values of the coefficient of variation of the α rhythm but strong within-group spreads. Of the active tests, the most stable indicators were obtained on observing motor actions and the least stable patterns were seen on performance. Writing with a pen was the action characterized by the lowest stability of EEG measures.
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Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 70, No. 1, pp, 40–49, January–February, 2020.
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Lebedeva, N.N., Karimova, E.D. Stability of Human EEG Patterns in Different Tasks: The Person Authentication Problem. Neurosci Behav Physi 50, 874–880 (2020). https://doi.org/10.1007/s11055-020-00980-5
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DOI: https://doi.org/10.1007/s11055-020-00980-5