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
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)


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.


Algorithm EEG EMG Fuzzy set Stimulated emotion Cognitive activity 


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© 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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