The task of creating a neural interface for controlling a robotic system by means of an oculographic interface and bioelectric signals, is considered. The article highlights the results of scientific experimental research aimed at the evaluation of the representativeness of bioelectrical signals obtained by electroencephalography (EEG). The basic hypothesis is formulated and tested with the help of artificial neural network technology. The authors consider an experiment on the formation of steady-state visually evoked potentials in a group of people with the subsequent creation of an applied database. They describe an original approach for extracting representative features from the EEG signal. With the help of deep machine learning technology the representativeness of the data under study is evaluated. The main conclusions are formulated and the hypothesis that each brain lead reproduces unique waves which are characteristic of each brain zone is confirmed. The proposed model of a symmetric multilayer multi-adaptive direct propagation neuron can find its application in solving problems related to the processing of EEG signals. Based on the results of this study, the authors suggest that data on the bioelectrical activity of the brain can be uniquely identified, and thus used as control signals for various robotic devices.