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Biotechnical System for the Study of Processes of Increasing Cognitive Activity Through Emotional Stimulation

  • Natalya FilatovaEmail author
  • Natalya Bodrina
  • Konstantin Sidorov
  • Pavel Shemaev
  • Gennady Vinogradov
Conference paper
  • 7 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)

Abstract

The article discusses the structure and software of the biotechnical system used to study the processes of human cognitive activity during the long-term execution of the same type of computational operations.

The methodical features of the experiments are considered. For the analysis of brain electrical activity, spectral analysis and nonlinear dynamics methods were used. Based on the evaluation of the power spectra, as well as the entropy of signals, the correlation dimension, estimates of maximum vectors and density of points in the center of attractors reconstructed from fragments of electroencephalograph (EEG) signals, signal descriptions for a sliding computation window were created. Given the number of random factors that can cause changes in the characteristics of EEG signals, further analysis was performed using fuzzy algorithms. Emotional stimulation causing weak positive or negative reactions was used to enhance cognitive activity. Control of emotional reactions was carried out using an additional channel for recording signals of electrical activity of facial muscles (EMG signals). The greatest effect was observed in the stimulation of negative emotions, the speed of performing computational operations after emotiogenic stimulation increased in all subjects, and the number of errors decreased. To interpret the assessments of the subject’s cognitive activity, Sugeno algorithm was used. The duration of emotion stimulation was determined using the Mamdani fuzzy inference algorithm.

The article presents the results of experimental studies of monitoring algorithms for 5 characteristics of the EEG and EMG signals, as well as the control algorithm for a specific type of cognitive activity.

Keywords

Algorithm EEG EMG Fuzzy set Stimulated emotion Cognitive activity 

References

  1. 1.
    Lapshina, T.N.: EEG-indication of emotional states of a person. Bull. Moscow State Univ. Psychol. Series 2, 101–102 (2004). (in Russian Vestnik MGU, seriya: Psihologia)Google Scholar
  2. 2.
    Davidson, R.J.: Affective style and affective disorders: perspectives from affective neuroscience. Cogn. Emot. 12(3), 307–330 (1998).  https://doi.org/10.1080/026999398379628CrossRefGoogle Scholar
  3. 3.
    Pomer-Escher, A., Tello, R., Castillo, J., Bastos-Filho, T.: Analysis of mental fatigue in motor imagery and emotional stimulation based on EEG. In: Proceedings of the XXIV Brazilian Congress of Biomedical Engineering “CBEB 2014”, pp. 1709–1712. Canal6, Brazil (2014)Google Scholar
  4. 4.
    Başar, E., Güntekin, B.: Review of delta, theta, alpha, beta, and gamma response oscillations in neuropsychiatric disorders. Suppl. Clin. Neurophysiol. 62, 303–341 (2013).  https://doi.org/10.1016/b978-0-7020-5307-8.00019-3CrossRefGoogle Scholar
  5. 5.
    Soininen, H., Partanen, J., Paakkonen, A., Koivisto, E., Riekkinen, P.J.: Changes in absolute power values of EEG spectra in the follow-up of Alzheimer’s disease. Acta Neurol. Scand. 83(2), 133–136 (1991).  https://doi.org/10.1111/j.1600-0404.1991.tb04662.xCrossRefGoogle Scholar
  6. 6.
    Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999).  https://doi.org/10.1016/s0165-0173(98)00056-3CrossRefGoogle Scholar
  7. 7.
    Filatova, N.N., Sidorov, K.V., Shemaev, P.D., Rebrun, I.A., Bodrina, N.I.: Analyzing video information by monitoring bioelectric signals. In: Abraham, A., et al. (Eds.): Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI 2018), Advances in Intelligent Systems and Computing 875, vol. 2, pp. 329–339. Springer, Switzerland (2019).  https://doi.org/10.1007/978-3-030-01821-4_35
  8. 8.
    Mirzakulova, S.A., Shuvalov, V.P., Mekler, A.A.: Studying network traffic using nonlinear dynamics methods. J. Theor. Appl. Inf. Technol. 95(21), 5869–5880 (2017)Google Scholar
  9. 9.
    Grissmann, S., Faller, J., Scharinger, C., Spuler, M., Gerjets, P.: Electroencephalography based analysis of working memory load and affective valence in an n-back task with emotional stimuli. Front. Hum. Neurosci. 11(616), 1–12 (2017).  https://doi.org/10.3389/fnhum.2017.00616CrossRefGoogle Scholar
  10. 10.
    Reiner, M., Rozengurt, R., Barnea, A.: Better than sleep: theta neurofeedback training accelerates memory consolidation. Biol. Psychol. 95, 45–53 (2014).  https://doi.org/10.1016/j.biopsycho.2013.10.010CrossRefGoogle Scholar
  11. 11.
    Filatova, N.N., Bodrina, N.I., Sidorov, K.V., Shemaev, P.D.: Organization of information support for a bioengineering system of emotional response research. In: Proceedings of the XX International Conference “Data Analytics and Management in Data Intensive Domains” DAMDID/RCDL. CEUR Workshop Proceedings, pp. 90–97. CEUR. Moscow, Russia (2018)Google Scholar
  12. 12.
    Sidorov, K.V., Filatova, N.N., Shemaev, P.D.: An interpreter of a human emotional state based on a neural-like hierarchical structure. In: Abraham, A., et al. (Eds.): Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI 2018), Advances in Intelligent Systems and Computing 874, vol. 1, pp. 483–492. Springer, Switzerland (2019).  https://doi.org/10.1007/978-3-030-01818-4_48
  13. 13.
    Filatova, N.N., Sidorov, K.V.: Computer models of emotions: construction and methods of research. Tver State Technical University (2017). (in Russian Kompyuternye Modeli Emotsy: Postroenie i Metody Issledovaniya)Google Scholar
  14. 14.
    Filatova, N.N., Sidorov, K.V., Shemaev, P.D.: Prediction properties of attractors based on their fuzzy trend. In: Abraham, A., et al. (eds.) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI 2017), Advances in Intelligent Systems and Computing 679, vol. 1, pp. 244–253. Springer, Switzerland (2018).  https://doi.org/10.1007/978-3-319-68321-8_25
  15. 15.
    Filatova, N.N., Sidorov, K.V., Shemaev, P.D., Iliasov, L.V.: Monitoring attractor characteristics as a method of objective estimation of testee’s emotional state. J. Eng. Appl. Sci. 12, 9164–9175 (2017).  https://doi.org/10.3923/jeasci.2017.9164.9175CrossRefGoogle Scholar
  16. 16.
    Rabinovich, M.I., Muezzinoglu, M.K.: Nonlinear dynamics of the brain: emotion and cognition. Adv. Phys. Sci. 180(4), 371–387 (2010).  https://doi.org/10.3367/ufnr.0180.201004b.0371. (in Russian Uspekhi Fizicheskikh Nauk)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Natalya Filatova
    • 1
    Email author
  • Natalya Bodrina
    • 1
  • Konstantin Sidorov
    • 1
  • Pavel Shemaev
    • 1
  • Gennady Vinogradov
    • 1
  1. 1.Tver State Technical UniversityTverRussia

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