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In this article we present the results of our research related to the studyof correlations between specific visual stimulation and the elicited brain'selectro-physiological response collected by EEG sensors from a group ofparticipants. We will look at how the various characteristics of visualstimulation affect the measured electro-physiological response of the brain anddescribe the optimal parameters found that elicit a steady-state visuallyevoked potential (SSVEP) in certain parts of the cerebral cortex where it canbe reliably perceived by the electrode of the EEG device. After that, wecontinue with a description of the advanced machine learning pipeline modelthat can perform confident classification of the collected EEG data in order to(a) reliably distinguish signal from noise (about 85% validation score) and (b)reliably distinguish between EEG records collected from different humanparticipants (about 80% validation score). Finally, we demonstrate that theproposed method works reliably even with an inexpensive (less than $100)consumer-grade EEG sensing device and with participants who do not haveprevious experience with EEG technology (EEG illiterate). All this incombination opens up broad prospects for the development of new types ofconsumer devices, [e.g.] based on virtual reality helmets or augmented realityglasses where EEG sensor can be easily integrated. The proposed method can beused to improve an online user experience by providing [e.g.] password-lessuser identification for VR / AR applications. It can also find a more advancedapplication in intensive care units where collected EEG data can be used toclassify the level of conscious awareness of patients during anesthesia or toautomatically detect hardware failures by classifying the input signal asnoise.

Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification