Nonlinear effect of biological feedback on brain attentional state

Abstract

A nonlinear effect of biological feedback on visual perception is studied when a brain–computer interface is applied. The implemented algorithm for estimation of visual attention is based on the time–frequency analysis of human electroencephalograms in real time by measuring the amplitude of the stimulus-related brain response, which takes subsequently positive and negative values. The analysis shows that time intervals with positive amplitude are associated with periods of sustained attention, whereas time intervals with negative amplitude are related to mental fatigue. The comparison of the results obtained in two groups of subjects, one without feedback and another with feedback, demonstrate that the feedback control prolongs the periods of sustained attention. The largest interval of sustained attention in the former group reached only \(100\pm 20\) s versus \(150\pm 40\) s in the latter group. However, the mean degree of attention, estimated by averaging the brain response amplitude over the whole interval, was 27% lower in the group with feedback than in another group. The obtained results evidence that cognitive resource is limited, and therefore, to maintain high performance for prolonged time, the brain has to work in a “safe-mode” regime.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. 1.

    Buzsaki, G., Draguhn, A.: Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004)

    Google Scholar 

  2. 2.

    Maksimenko, V.A., Pavlov, A., Runnova, A.E., Nedaivozov, V., Grubov, V., Koronovskii, A.A., Pchelintseva, S.V., Pitsik, E., Pisarchik, A.N., Hramov, A.E.: Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects. Nonlinear Dyn. 91(4), 2803–2817 (2018)

    Google Scholar 

  3. 3.

    Lopes da Silva, F.H., Nunez, P.L., Srinivasan, K.: Electric Fields of the Brain: The Neurophysics of EEG. Oxford University Press, Oxford (2006)

    Google Scholar 

  4. 4.

    Luders, H., Noachtar, S.: Atlas and Classification of Electroencephalography. WB Saunders Co., Philadelphia (2000)

    Google Scholar 

  5. 5.

    Guirao, J.L.G., Luo, A.C.J.: New trends in nonlinear dynamics and chaoticity. Nonlinear Dyn. 84, 1–2 (2016)

    MathSciNet  Google Scholar 

  6. 6.

    Gao, J., Hu, J., Tung, W.W.: Entropy measures for biological signal analyses. Nonlinear Dyn. 68, 431–444 (2012)

    MathSciNet  MATH  Google Scholar 

  7. 7.

    Yan, J., Wang, Y., Ouyang, G., Yu, T., Li, Y., Sik, A., Li, X.: Analysis of electrocorticogram in epilepsy patients in terms of criticality. Nonlinear Dyn. 83, 1909–1917 (2016)

    MathSciNet  Google Scholar 

  8. 8.

    Daly, D., Pedley, T.A.: Current Practice of Clinical Electroencephalography. Raven Press, New York (1990)

    Google Scholar 

  9. 9.

    Spuler, M.: A high-speed brain-computer interface (BCI) using dry EEG electrodes. PLoS ONE 12(2), 1–12 (2017)

    Google Scholar 

  10. 10.

    Bowsher, K., Civillico, E., Coburn, J., Collinger, J., Contreras-Vidal, J., Denison, T., Donoghue, J., French, J., Getzoff, N., Hochberg, L., et al.: Brain-computer interface devices for patients with paralysis and amputation: a meeting report. J. Neural Eng. 13(2), 023001 (2016)

    Google Scholar 

  11. 11.

    Zhang, Y., Yin, E., Li, F., Zhang, Y., Tanaka, T., Zhao, Q., Cui, Y., Xu, P., Yao, D., Guo, D.: Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 26(7), 1314–1323 (2018)

    Google Scholar 

  12. 12.

    Zhang, Y., Guo, D., Li, F., Yin, E., Zhang, Y., Li, P., Zhao, Q., Tanaka, T., Yao, D., Xu, P.: Correlated component analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 26(5), 948–956 (2018)

    Google Scholar 

  13. 13.

    Hochberg, L.R., Serruya, M.D., Friehs, G.M., Mukand, J.A., Saleh, M., Caplan, A.H., Branner, A., Chen, D., Penn, R.D., Donoghue, J.P.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006)

    Google Scholar 

  14. 14.

    Chaudhary, U., Birbaumer, N., Ramos-Murguialday, A.: Brain-computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 12, 513–525 (2016)

    Google Scholar 

  15. 15.

    De Nunzio, A.M., Dosen, S., Lemling, S., Markovic, M., Schweisfurth, M.A., Ge, N., Graimann, B., Falla, D., Farina, D.: Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels. Exp. Brain Res. 235(8), 2547–2559 (2017)

    Google Scholar 

  16. 16.

    Gonzalez, J., Soma, H., Sekine, M., Yu, W.: Psycho-physiological assessment of a prosthetic hand sensory feedback system based on an auditory display: a preliminary study. J. NeuroEng. Rehabil. 9(33), 1–14 (2012)

    Google Scholar 

  17. 17.

    Raspopovic, S., Capogrosso, M., Petrini, F.M., Bonizzato, M., Rigosa, J., Di Pino, G., Carpaneto, J., Controzzi, M., Boretius, T., Fernndez, E., Granata, G., Oddo, C.M., Citi, L., Ciancio, A.L., Cipriani, C., Carrozza, M.C., Jensen, W., Guglielmelli, E., Stieglitz, T., Rossini, P.M., Micera, S.: Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci. Transl. Med. 6, 222 (2014)

    Google Scholar 

  18. 18.

    De Pascalis, V., Silveri, A.: Effects of feedback control on EEG alpha asymmetry during covert mental tasks. Int. J. Psychophysiol. 3, 163–170 (1986)

    Google Scholar 

  19. 19.

    Touron, D.R., Hertzog, C.: Accuracy and speed feedback: global and local effects on strategy use. Exp. Aging Res. 40, 332–356 (2014)

    Google Scholar 

  20. 20.

    Yagasaki, K.: Chaos in a pendulum with feedback control. Nonlinear Dyn. 6, 125–142 (1994)

    Google Scholar 

  21. 21.

    Yagasaki, K.: A simple feedback control system: bifurcations of periodic orbits and chaos. Nonlinear Dyn. 9, 391–417 (1996)

    Google Scholar 

  22. 22.

    Yabuno, H.: Bifurcation control of parametrically excited duffing system by a combined linear-plus-nonlinear feedback control. Nonlinear Dyn. 12(3), 263–274 (1997)

    MathSciNet  MATH  Google Scholar 

  23. 23.

    Martnez-Zrega, B.E., Pisarchik, A.N., Tsimring, L.S.: Using periodic modulation to control coexisting attractors induced by delayed feedback. Phys. Lett. A 318, 102–111 (2003)

    MathSciNet  MATH  Google Scholar 

  24. 24.

    Pisarchik, A.N., Feudel, U.: Control of multistability. Phys. Rep. 540, 167–218 (2014)

    MathSciNet  MATH  Google Scholar 

  25. 25.

    Zhu, W.Q., Ying, Z.G., Soong, T.T.: An optimal nonlinear feedback control strategy for randomly excited structural systems. Nonlinear Dyn. 24, 31–51 (2001)

    MathSciNet  MATH  Google Scholar 

  26. 26.

    Masoud, Z.N., Nayfeh, A.H.: Sway reduction on container cranes using delayed feedback controller. Nonlinear Dyn. 34, 347–358 (2003)

    MATH  Google Scholar 

  27. 27.

    Bhoir, N., Singh, S.N.: Output feedback modular adaptive control of a nonlinear prototypical wing section. Nonlinear Dyn. 37, 357373 (2004)

    MathSciNet  MATH  Google Scholar 

  28. 28.

    Sevilla-Escoboza, R., Pisarchik, A.N., Jaimes-Reategui, R., Huerta-Cuellar, G.: Selective monostability in multi-stable systems. Proc. R. Soc. Lon. A 471(2180), 1–15 (2015)

    MathSciNet  MATH  Google Scholar 

  29. 29.

    Sevilla-Escoboza, R., Huerta-Cuellar, G., Jaimes-Reategui, R., Medel-Ruiz, C.I., Castaneda, C.E., Lopez-Mancilla, D., Pisarchik, A.N.: Error-feedback control of multistability. J. Franklin Inst. 354(16), 7346–7358 (2017)

    MathSciNet  MATH  Google Scholar 

  30. 30.

    Maksimenko, V.A., Runnova, A.E., Zhuravlev, M.O., Nedaivozov, V., Grubov, V.V., Pchelintseva, S.V., Hramov, A.E., Pisarchik, A.N.: Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface. PLoS ONE 12(12), 1–20 (2017)

    Google Scholar 

  31. 31.

    Necker, L.A.: Observations on some remarkable phenomena seen in switzerland; and an optical phenomenon which occurs on viewing of a crystal or geometrical solid. Philos. Mag. 3, 329–343 (1832)

    Google Scholar 

  32. 32.

    Kornmeier, J., Pfaffle, M., Bach, M.: Necker cube: stimulus-related (low-level) and percept-related (high-level) EEG signatures early in occipital cortex. J. Vis. 11(9), 12 (2011)

    Google Scholar 

  33. 33.

    Mathes, B., Strber, D., Stadler, M.A., Basar-Eroglu, C.: Voluntary control of necker cube reversals modulates the EEG delta-and gamma-band response. Neurosci. Lett. 402(1), 145–149 (2006)

    Google Scholar 

  34. 34.

    Pisarchik, A.N., Jaimes-Reategui, R., Magalln-Garcia, C.D.A., Castillo-Morales, C.O.: Critical slowing down and noise-induced intermittency in bistable perception: bifurcation analysis. Biol. Cyber. 108(4), 397–404 (2014)

    Google Scholar 

  35. 35.

    Pisarchik, A.N., Bashkirtseva, I.A., Ryashko, L.: Controlling bistability in a stochastic perception model. Eur. Phys. J. Spec. Top. 224(8), 1477–1484 (2015)

    Google Scholar 

  36. 36.

    Bashkirtseva, I.A., Ryashko, L.: Stochastic sensitivity of a bistable energy model for visual perception. Indian J. Phys. 91(1), 57–62 (2017)

    Google Scholar 

  37. 37.

    Wang, M., Arteaga, D., He, B.J.: Brain mechanisms for simple perception and bistable perception. Proc. Nat. Acad. Sci. 110(35), E3350–E3359 (2013)

    Google Scholar 

  38. 38.

    Mulckhuyse, M., Kelley, T.A., Theeuwes, J., Walsh, V., Lavie, N.: Enhanced visual perception with occipital transcranial magnetic stimulation. Eur. J. Neurosci. 34(8), 1320–1325 (2011)

    Google Scholar 

  39. 39.

    Gleiss, S., Kayser, C.: Acoustic noise improves visual perception and modulates occipital oscillatory states. J. Cogn. Neurosci. 26(4), 699–711 (2014)

    Google Scholar 

  40. 40.

    Laufs, H., Holt, J.L., Elfont, R., Krams, M., Paul, J.S., Krakow, K., Kleinschmidt, A.: Where the BOLD signal goes when alpha EEG leaves. Neuroimage 31(4), 1408–1418 (2006)

    Google Scholar 

  41. 41.

    Niedermeyer, E., Lopes da Silva, F.H. (eds.): Electroencephalography. Basic Principles, Clinical Applications, and Related Fields, 5th edn. Lippincott, Williams & Wilkins, Philadelphia (2005)

    Google Scholar 

  42. 42.

    Hramov, A.E., Maksimenko, V.A., Pchelintseva, S.V., Runnova, A.E., Grubov, V.V., Musatov, V.Y., Zhuravlev, M.O., Koronovskii, A.A., Pisarchik, A.N.: Classifying the perceptual interpretations of a bistable image using EEG and artificial neural networks. Front. Neurosci. 11, 674 (2017)

    Google Scholar 

  43. 43.

    Leopold, D.A., Wilke, M., Maier, A., Logothetis, N.K.: Stable perception of visually ambiguous patterns. Nat. Neurosci. 5(6), 605–609 (2002)

    Google Scholar 

  44. 44.

    Kornmeier, J., Ehn, W., Bigalke, H., Bach, M.: Discontinuous presentation of ambiguous figures: How interstimulus-interval durations affect reversal dynamics and ERPs. Psychophysiol. 44(4), 552–560 (2007)

    Google Scholar 

  45. 45.

    Pavlov, A.N., Hramov, A.E., Koronovskii, A.A., Sitnikova, Y.E., Makarov, V.A., Ovchinnikov, A.A.: Wavelet analysis in neurodynamics. Physics-Uspekhi 55(9), 845–875 (2012)

    Google Scholar 

  46. 46.

    Hramov, A.E., Koronovskii, A.A., Makarov, V.A., Pavlov, A.N., Sitnikova, E.: Wavelets in Neuroscience. Springer, Berlin (2015)

    MATH  Google Scholar 

  47. 47.

    Ogden, T.: Essential Wavelets for Statistical Applications and Data Analysis. Springer, New York (2012)

    Google Scholar 

  48. 48.

    Sitnikova, E., Hramov, A.E., Grubov, V., Koronovsky, A.A.: Time-frequency characteristics and dynamics of sleep spindles in WAG/Rij rats with absence epilepsy. Brain Res. 1543, 290–299 (2014)

    Google Scholar 

  49. 49.

    Maksimenko, V.A., Runnova, A.E., Frolov, N.S., Makarov, V.V., Nedaivozov, V.O., Koronovskii, A.A., Pisarchik, A.N., Hramov, A.E.: Multiscale neural connectivity during human sensory processing in the brain. Phys. Rev. E 97, 052405 (2018)

    Google Scholar 

  50. 50.

    Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.M., Kennedy, H., Fries, P.: Alpha–beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron 384, 384–397 (2016)

    Google Scholar 

  51. 51.

    Buffalo, E.A., Fries, P., Landman, R., Buschman, T.J., Desimone, R.: Laminar differences in gamma and alpha coherence in the ventral stream. Proc. Natl. Acad. Sci. United States of Am. 108(11), 262–267 (2011)

    Google Scholar 

  52. 52.

    Roy, R.N., Charbonnier, S., Campagne, A., Bonnet, S.: Efficient mental workload estimation using task-independent EEG features. J. Neural Eng. 13, 026019 (2016)

    Google Scholar 

  53. 53.

    Chaudhuri, A., Behan, P.O.: Fatigue and basal ganglia. J. Neurolog. Sci. 179(1–2), 34–42 (2000)

    Google Scholar 

  54. 54.

    Mizuno, K., Tanaka, M., Yamaguti, K., Kajimoto, O., Kuratsune, H., Watanabe, Y.: Mental fatigue caused by prolonged cognitive load associated with sympathetic hyperactivity. Behav. Brain Fun. 7(1), 17 (2011)

    Google Scholar 

  55. 55.

    Bonnefond, A., Doignon-Camus, N., Touzalin-Chretien, P., Dufour, A.: Vigilance and intrinsic maintenance of alert state: an ERP study. Behav. Brain Res. 211(2), 185–190 (2010)

    Google Scholar 

  56. 56.

    Boksem, M.A., Meijman, T.F., Lorist, M.M.: Effects of mental fatigue on attention: an ERP study. Cogn. Brain Res. 25(1), 107–116 (2005)

    Google Scholar 

  57. 57.

    Kato, Y., Endo, H., Kizuka, T.: Mental fatigue and impaired response processes: event-related brain potentials in a go/nogo task. Int. J. Psychophysiol. 72(2), 204–211 (2009)

    Google Scholar 

  58. 58.

    Faber, L.G., Maurits, N.M., Lorist, M.M.: Mental fatigue affects visual selective attention. PloS ONE 7(10), e48,073 (2012)

    Google Scholar 

  59. 59.

    Guo, Z., Chen, R., Zhang, K., Pan, Y., Wu, J.: The impairing effect of mental fatigue on visual sustained attention under monotonous multi-object visual attention task in long durations: an event-related potential based study. PloS ONE 11(9), e0163360 (2016)

    Google Scholar 

  60. 60.

    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)

    Google Scholar 

  61. 61.

    Tanaka, M., Shigihara, Y., Ishii, A., Funakura, M., Kanai, E., Watanabe, Y.: Effect of mental fatigue on the central nervous system: an electroencephalography study. Behav. Brain Funct. 8(1), 48 (2012)

    Google Scholar 

  62. 62.

    Zhao, C., Zhao, M., Liu, J., Zheng, C.: Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. Accid. Anal. Prev. 45, 83–90 (2012)

    Google Scholar 

  63. 63.

    Baars, B.J.: In the theatre of consciousness. global workspace theory, a rigorous scientific theory of consciousness. J. Conscious. Stud. 4(4), 292–309 (1997)

    Google Scholar 

  64. 64.

    Dehaene, S., Kerszberg, M., Changeux, J.P.: A neuronal model of a global workspace in effortful cognitive tasks. Proc. Natl. Acad. Sci. 95(24), 14529–14534 (1998)

    Google Scholar 

  65. 65.

    Finc, K., Bonna, K., Lewandowska, M., Wolak, T., Nikadon, J., Dreszer, J., Duch, W., Kühn, S.: Transition of the functional brain network related to increasing cognitive demands. Hum. Brain Mapp. 38(7), 3659–3674 (2017)

    Google Scholar 

  66. 66.

    Guo, D., Guo, F., Zhang, Y., Li, F., Xia, Y., Xu, P., Yao, D.: Periodic visual stimulation induces resting-state brain network reconfiguration. Front. Comput. Neurosci. 12, 21 (2018)

    Google Scholar 

  67. 67.

    Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. 102(27), 9673–9678 (2005)

    Google Scholar 

  68. 68.

    Corbetta, M., Shulman, G.L.: Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3(3), 201 (2002)

    Google Scholar 

  69. 69.

    Wojciulik, E., Kanwisher, N.: The generality of parietal involvement in visual attention. Neuron 23(4), 747–764 (1999)

    Google Scholar 

  70. 70.

    Mckiernan, K.A., Kaufman, J.N., Kucera-Thompson, J., Binder, J.R.: A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. J. Cogn. Neurosci. 15(3), 394–408 (2003)

    Google Scholar 

  71. 71.

    Marois, R., Ivanoff, J.: Capacity limits of information processing in the brain. Trends in Cogn. Sci. 9(6), 296–305 (2005)

    Google Scholar 

  72. 72.

    Vogel, E.K., Machizawa, M.G.: Neural activity predicts individual differences in visual working memory capacity. Nature 428(6984), 748 (2004)

    Google Scholar 

  73. 73.

    Todd, J.J., Marois, R.: Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428(6984), 751 (2004)

    Google Scholar 

  74. 74.

    Gross, J., Schmitz, F., Schnitzler, I., Kessler, K., Shapiro, K., Hommel, B., Schnitzler, A.: Modulation of long-range neural synchrony reflects temporal limitations of visual attention in humans. Proc. Natl. Acad. Sci. 101(35), 13050–13055 (2004)

    Google Scholar 

  75. 75.

    Marois, R., Chun, M.M., Gore, J.C.: Neural correlates of the attentional blink. Neuron 28(1), 299–308 (2000)

    Google Scholar 

  76. 76.

    Taya, F., Sun, Y., Babiloni, F., Thakor, N., Bezerianos, A.: Brain enhancement through cognitive training: a new insight from brain connectome. Front. Sys. Neurosci. 9, 44 (2015)

    Google Scholar 

  77. 77.

    Klingberg, T.: Training and plasticity of working memory. Trends Cogn. Sci. 14(7), 317–324 (2010)

    Google Scholar 

  78. 78.

    Jolles, D., Crone, E.A.: Training the developing brain: a neurocognitive perspective. Fron. Hum. Neurosci. 6, 76 (2012)

    Google Scholar 

  79. 79.

    Hempel, A., Giesel, F.L., Garcia Caraballo, N.M., Amann, M., Meyer, H., Wüstenberg, T., Essig, M., Schröder, J.: Plasticity of cortical activation related to working memory during training. Am. J. Psychiatry 161(4), 745–747 (2004)

    Google Scholar 

  80. 80.

    Olesen, P.J., Westerberg, H., Klingberg, T.: Increased prefrontal and parietal activity after training of working memory. Nat. Neurosci. 7(1), 75 (2004)

    Google Scholar 

  81. 81.

    Draganski, B., May, A.: Training-induced structural changes in the adult human brain. Behav. Brain Res. 192(1), 137–142 (2008)

    Google Scholar 

  82. 82.

    Driemeyer, J., Boyke, J., Gaser, C., Büchel, C., May, A.: Changes in gray matter induced by learning-revisited. PloS One 3(7), e2669 (2008)

    Google Scholar 

  83. 83.

    Scholz, J., Klein, M.C., Behrens, T.E., Johansen-Berg, H.: Training induces changes in white-matter architecture. Nat. Neurosci. 12(11), 1370 (2009)

    Google Scholar 

  84. 84.

    Wolf, D., Fischer, F.U., Fesenbeckh, J., Yakushev, I., Lelieveld, I.M., Scheurich, A., Schermuly, I., Zschutschke, L., Fellgiebel, A.: Structural integrity of the corpus callosum predicts long-term transfer of fluid intelligence-related training gains in normal aging. Hum. Brain Mapp. 35(1), 309–318 (2014)

    Google Scholar 

  85. 85.

    Maksimenko, V.A., Lu’uttjohann, A., Makarov, V.V., Goremyko, M.V., Koronovskii, A.A., Nedaivozov, V., Runnova, A.E., van Luijtelaar, G., Hramov, A.E., Boccaletti, S.: Macroscopic and microscopic spectral properties of brain networks during local and global synchronization. Phys. Rev. E 96, 012316 (2017)

    Google Scholar 

  86. 86.

    Rubchinsky, L.L., Park, C., Worth, R.M.: Intermittent neural synchronization in parkinsons disease. Nonlinear Dyn. 68, 329346 (2012)

    Google Scholar 

  87. 87.

    Lehnertz, K.: Epilepsy: Extreme Events in the Human Brain. Springer, Berlin, Heidelberg (2006)

    Google Scholar 

  88. 88.

    Pisarchik, A.N., Grubov, V.V., Maksimenko, V.A., Lttjohann, A., Frolov, N.S., Marqués-Pascual, C., Gonzalez-Nieto, D., Khramova, M., Hramov, A.E.: Extreme events in epileptic EEG of rodents after ischemic stroke. Eur. Phys. J. Spec. Top. 227(7–9), 3921–932 (2018)

    Google Scholar 

  89. 89.

    Kinreich, S., Djalovski, A., Kraus, L., Louzoun, Y., Feldman, R.: Brain-to-brain synchrony during naturalistic social interactions. Sci. Rep. 7(1), 17060 (2017)

    Google Scholar 

Download references

Acknowledgements

This work has been supported by the Russian Science Foundation (Grant 17-72-30003) in the part of experimental studies and intelligent control system realization for BCI. V.A.M. thanks President Program (project MK-992.2018.2) for personal support in the part of biological feedback influence analysis. A.N.P. acknowledges support from the Spanish Ministry of Economy and Competitiveness (project SAF2016-80240) in the part of neurophysiological experimental design preparation.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Alexander E. Hramov.

Ethics declarations

Human participants

Subjects participated in the experiment on a voluntary and gratuitous basis. All participants signed an informed medical consent to participate in the experimental work and received all necessary explanations about the process, including their agreement for further publication of the results. Acquired experimental data were processed with respect the confidentiality and anonymity of research respondents. The experimental studies were performed in accordance with the Declaration of Helsinki and approved by the local research Ethics Committee of the Yuri Gagarin State Technical University of Saratov.

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Maksimenko, V.A., Hramov, A.E., Grubov, V.V. et al. Nonlinear effect of biological feedback on brain attentional state. Nonlinear Dyn 95, 1923–1939 (2019). https://doi.org/10.1007/s11071-018-4668-1

Download citation

Keywords

  • Visual attention
  • EEG analysis
  • Brain–computer interface
  • Biological feedback