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

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OpenAlex trends: Human-Automation Interaction and Safety, Technology and Human Factors in Education and Health, Advanced Research in Systems and Signal Processing

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Number of citations: 0

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Human-Automation Interaction and Safety

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Technology and Human Factors in Education and Health

This cluster of papers explores the future of personalized medicine in healthcare, focusing on topics such as artificial intelligence, healthcare technology, medical innovation, ergonomics, biomedical signal analysis, digital health, cyber-physical systems, healthcare automation, and clinical decision support. The papers cover a wide range of interdisciplinary research related to advancing personalized medical treatments and technologies.

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Advanced Research in Systems and Signal Processing

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Article type: scientific article

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

Received 12.07.2022

Accepted

Published

For citation: Yuryev, G.A., Kuravsky, L.S., Yuryeva, N.E. (2022). On the Experience of Developing a Mobile Complex for Recording the Brain Electrical Activity on the Meringue of Dry Electrode Technology. Modelling and Data Analysis, 12(3), 40–48. (In Russ.). https://doi.org/10.17759/mda.2022120303

© Yuryev G.A., Kuravsky L.S., Yuryeva N.E., 2022

License: CC BY-NC 4.0

References

  1. Brown, E. (2014). Web Development with Node and Express. Germany: O'Reilly.
  2. Greshnikov I.I., Kuravsky L.S., Yuryev G.A. Principles of Developing a Software and Hardware Complex for Crew Intelligent Support and Training Level Assessment. Modelirovanie i analiz dannykh = Modelling and Data Analysis, 2021. Vol. 11, no. 2, pp. 5–30. DOI: https://doi.org/10.17759/mda.2021110201 (In Russ., аbstr. in Engl.).
  3. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I., Greshnikov I.I., and Polyakov B.Y. An approach to diagnostics based on video oculography data analysis. – The British Institute of Non-Destructive Testing, 17th International Conference on Condition Monitoring and Asset Management, June 2021.
  4. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I., Yuryeva N.E. Assessing the Aircraft Crew Actions with the Aid of a Human Factor Risk Model. Eksperimental’naya psikhologiya = Experimental Psychology (Russia), 2020. Vol. 13, no. 2, pp. 153—181. DOI: https://doi.org/10.17759/exppsy.2020130211.
  5. Niedermeyer E. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields / Niedermeyer E., da Silva F.L.. — Lippincott Williams & Wilkins, 2004.
  6. Warner, R. M. (1998). Spectral analysis of time-series data. New York: Guilford Press.

Information About the Authors

Grigory A. Yuryev, Candidate of Science (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, Russian Federation, 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, Russian Federation, ORCID: https://orcid.org/0000-0002-3375-8446, e-mail: l.s.kuravsky@gmail.com

Nataliya E. Yuryeva, Candidate of Science (Engineering), Head of the Laboratory of Information Technologies for Psychological Diagnostics, Research Fellow of the Laboratory of Quantitative Psychology of the Center for Information Technologies for Psychological Research of the Faculty of Information Technology, Executive Secretary of the journal "Modeling and Data Analysis", Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-1419-876X, e-mail: yurieva.ne@gmail.com

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