Evaluation of representativeness of EEG data for zonal affiliation of brain waves by leads

Daniyar Wolf; Yaroslav Turovsky; Anastasia Olegovna Iskhakova; Roman Meshcheryakov

Daniyar Wolf V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences http://orcid.org/0000-0001-5021-5411
Yaroslav Turovsky V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences http://orcid.org/0000-0002-5290-885X
Anastasia Olegovna Iskhakova V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences http://orcid.org/0000-0001-8358-298X
Roman Meshcheryakov V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences http://orcid.org/0000-0002-1129-8434

Keywords: Brain-Computer Interface, Bioelectrical Signals, Electroencephalography, Steady-State Visual Evoked Potentials, Machine Learning

Abstract

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, which provides functions of encoder and decoder, 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 the data on bioelectrical activity of the brain recorded by EEG can be represented as a multidimensional random variable, where the center position is also the mathematical expectation of its projections on the axes of the principal components.