On the Experience of Developing a Mobile Complex for Recording the Brain Electrical Activity on the Meringue of Dry Electrode Technology

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Abstract

The technology of creating a mobile complex for registration of the brain electrical activity, for recording an electroencephalographic signal with one channel, is considered. Previously, technological problems associated with insufficient sensitivity and selectivity in the sense of signal-to-noise ratio did not allow the use of electroencephalographic activity recording systems based on the so-called dry electrodes in practical applications. At the same time, even with a small number of leads, such signals, when recorded, for example, from the visual cortex localized in the occipital region of the brain can be extremely informative in the context of the operator activity analysis and other types of human activity, in which arbitrary control of attention plays an essential role. This paper considers the experience of creating a mobile autonomous complex for recording such signals for the tasks of monitoring the characteristics of the operator's activities in scientific applications. The design features of such a device, created based on Node MCU technology, which is gaining wide distribution in embedded systems, proprietary narrowband amplifiers of the electric signal and dry electrodes created by industry in the last decade, are described. Some examples of practical application of such a complex are given. The most promising directions for the development of technology are discussed.

General Information

Keywords: human-machine interfaces, electroencephalography, dry electrodes, Node MCU

Journal rubric: Software

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2022120303

Received: 12.07.2022

Accepted:

For citation: Yuryev G.A., Kuravsky L.S., Yuryeva N.E. On the Experience of Developing a Mobile Complex for Recording the Brain Electrical Activity on the Meringue of Dry Electrode Technology. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2022. Vol. 12, no. 3, pp. 40–48. DOI: 10.17759/mda.2022120303. (In Russ., аbstr. in Engl.)

References

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Information About the Authors

Grigory A. Yuryev, PhD in Physics and Matematics, Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-2960-6562, e-mail: g.a.yuryev@gmail.com

Lev S. Kuravsky, Doctor of Engineering, professor, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-3375-8446, e-mail: l.s.kuravsky@gmail.com

Nataliya E. Yuryeva, PhD in Engineering, Head of Laboratory, Youth Laboratory Information Technologies for Psychological Diagnostics, Research Fellow, Information Technology Center for Psychological Studies of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0003-1419-876X, e-mail: yurieva.ne@gmail.com

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