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Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects

Abstract

Identification of brain activity associated with motor execution and, more importantly, with motor imagery is necessary for the development of brain–computer interfaces. Most of recent studies were performed with trained participants which demonstrated that the motor-related brain activity can be detected from the analysis of multichannel electroencephalograms (EEG). For untrained subjects, this task is less studied, but at the same time much more challenging. This task can be solved using the methods of nonlinear dynamics, allowing to extract specific features of the neuronal network of the brain (e.g., the degree of complexity of EEG signals and degree of interaction between different brain areas). In this work, we analyze the spatio-temporal and time–frequency characteristics of the electrical brain activity, associated with both the motor execution and imagery in a group of untrained subjects, by applying different methods of nonlinear dynamics. At the first stage, we apply multifractal formalism to the analysis of EEG signals to reveal the brain areas which demonstrate the most significant distinctions between real motor actions and imaginary movement. Then, using time–frequency wavelet-based analysis of the EEG activity, we analyze in detail the structure of considered brain areas. As a result, we distinguish characteristic oscillatory patterns which occur in different areas of brain and interact with each other when the motor execution (or imagination) takes place. Finally, we create an algorithm allowing online detection of the observed patterns and experimentally verify its efficiency.

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References

  1. Kawase, T., et al.: A Hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements. J. Neural Eng. 14, 016015 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Bowsher, K., et al.: Brain–computer interface devices for patients with paralysis and amputation: a meeting report. J. Neural Eng. 13, 023001 (2016)

    Article  Google Scholar 

  4. Chen, X., et al.: High-speed spelling with a noninvasive brain–computer interface. Proc. Natl. Acad. Sci. 112, 44 (2015)

    Google Scholar 

  5. O’Doherty, J.E., et al.: Active tactile exploration using a brain-machine-brain interface. Nature 479, 228 (2011)

    Article  Google Scholar 

  6. Stacey, W.C., et al.: Technology Insight: neuroengineering and epilepsy—designing devices for seizure control. Nat. Rev. 4, 4 (2008)

    Google Scholar 

  7. Wolpaw, J., McFarland, D.J.: Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc. Natl Acad. Sci. USA 101, 17849 (2004)

    Article  Google Scholar 

  8. Birbaumer, N., et al.: A spelling device for the paralyzed. Nature 398b, 297 (2000). 8

    Google Scholar 

  9. Ma, T., et al.: The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential. J. Neural Eng. 14, 026015 (2017)

    Article  Google Scholar 

  10. Daly, J.J.: Brain–computer interfaces in neurological rehabilitation. Lancet Neurol. 7, 1032 (2008)

    Article  Google Scholar 

  11. Peternel, L., et al.: Adaptive control of exoskeleton robots for periodic assistive behaviors based on EMG feedback minimization. PLoS ONE 11, 2 (2016)

    Article  Google Scholar 

  12. Maksimenko, V.A., et al.: Absence seizure control by a brain computer interface. Sci. Rep. 7, 2487 (2017)

    Article  Google Scholar 

  13. Kaplan, A.Y., et al.: Adapting the P300-based brain–computer interface for gaming: a review. IEEE Trans. Comput. Intell. AI Games 5, 141 (2013)

    Article  Google Scholar 

  14. Wessberg, J., et al.: Neuroscience: brain-controlled robot grabs attention. Nature 408, 361 (2000)

    Article  Google Scholar 

  15. Serruya, M.D., et al.: Instant neural control of a movement signal. Nature 416, 141 (2002)

    Article  Google Scholar 

  16. Taylor, D.A., et al.: Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829 (2002)

    Article  Google Scholar 

  17. Birbaumer, M., et al.: A spelling device for the paralyzed. Nature 398, 297 (1999)

    Article  Google Scholar 

  18. Melnik, A., et al.: EEG correlates of sensorimotor processing: independent components involved in sensory and motor processing. Sci. Rep. 7, 4461 (2017)

    Article  Google Scholar 

  19. Maximenko, V.A., et al.: Macroscopic and microscopic spectral properties of brain networks during local and global synchronization. Phys. Rev. E 96, 012316 (2017)

    Article  Google Scholar 

  20. Jalili, M.: Functional brain networks: does the choice of dependency estimator and binarization method matter. Sci. Rep. 6, 29780 (2016)

    Article  Google Scholar 

  21. Vasilyev, A., et al.: Assessing motor imagery in brain–computer interface training: psychological and neurophysiological correlates. Neuropsychologia 97, 56 (2017)

    Article  Google Scholar 

  22. Basyul, I.A., et al.: Changes in the N200 and P300 components of event-related potentials on variations in the conditions of attention in a brain-computer interface system. Neurosci. Behav. Physiol. 45(9), 1038 (2015)

    Article  Google Scholar 

  23. Ma, T., et al.: The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing. J. Neurosci. Methods 275, 80 (2017)

    Article  Google Scholar 

  24. Quitadamo, L.R., et al.: Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review. J. Neural Eng. 14, 011001 (2017)

    Article  Google Scholar 

  25. Wang, Y., et al.: Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications. J. Neuroeng. Rehabil. 10, 109 (2013)

    Article  Google Scholar 

  26. Hamedi, M., et al.: Electroencephalographic motor imagery brain connectivity analysis for BCI: a review. Neural Comput. 28(6), 999 (2016)

    MathSciNet  Article  Google Scholar 

  27. Wolpaw, J.R., McFarland, D.J.: Control of a two-dimensional movement signal by a non-invasive brain–computer interface in humans. Proc. Natl. Acad. Sci. USA 101(51), 1784917854 (2004)

    Article  Google Scholar 

  28. McFarland, D.J., et al.: Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr. 12, 3 (2000)

    Article  Google Scholar 

  29. Blankertz, B., Dornhege, G., Krauledat, M., Müller, K.R., Curio, G.: The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. NeuroImage 37, 539550 (2007)

    Article  Google Scholar 

  30. Ferrante, A., Gavriel, C., Faisal, A.: Data-efficient hand motor imagery decoding in EEG-BCI by using Morlet wavelets & common spatial pattern algorithms. In: 7th International IEEE/EMBS Conference on Neural Engineering (NER), vol. 948 (2015)

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

    MathSciNet  Article  Google Scholar 

  32. Gao, J., Hu, J., Tung, W.-W.: Entropy measures for biological signal analyses. Nonlinear Dyn. 68(3), 431444 (2012)

    MathSciNet  Article  MATH  Google Scholar 

  33. Rubchinsky, L.L., Park, C., Worth, R.M.: Intermittent neural synchronization in Parkinsons disease. Nonlinear Dyn. 68(3), 329346 (2012)

    Article  Google Scholar 

  34. 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, 19091917 (2016)

    MathSciNet  Article  Google Scholar 

  35. Wu, Y.-T., Shyu, K.-K., Chen, T.-R., Guo, W.-Y.: Using three-dimensional fractal dimension to analyze the complexity of fetal cortical surface from magnetic resonance images. Nonlinear Dyn. 58, 745 (2009)

    Article  MATH  Google Scholar 

  36. Niedermeyer, E., da Silva, F.L.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Nonlinear Dynamics. Lippincot Williams & Wilkins, Philadelphia (2004)

    Google Scholar 

  37. Muzy, J.F., Bacry, E., Arneodo, A.: Wavelets and multifractal formalism for singular signals: application to turbulence data. Phys. Rev. Lett. 67, 3515 (1991)

    Article  Google Scholar 

  38. Muzy, J.F., Bacry, E., Arneodo, A.: Multifractal formalism for fractal signals: the structure function approach versus the wavelet transform modulus maxima method. Phys. Rev. E 47, 875 (1993)

    Article  Google Scholar 

  39. Ivanov, P.C.H., Amaral, L.A.N., Goldberger, A.L., Havlin, S., Rosenblum, M.G., Struzik, Z.R., Stanley, H.E.: Multifractality in human heartbeat dynamics. Nature 399, 461465 (1999)

    Article  Google Scholar 

  40. Pavlov, A.N., Anishchenko, V.S.: Multifractal analysis of complex signals. Phys. Uspekhi 50, 819834 (2007)

    Article  Google Scholar 

  41. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. 454, 903 (1998)

    MathSciNet  Article  MATH  Google Scholar 

  42. Grubov, V.V., et al.: Automatic extraction and analysis of oscillatory patterns on nonstationary EEG signals by means of wavelet transform and the empirical modes method. BRAS Phys. 76, 1361–1364 (2012)

    Google Scholar 

  43. Grubov, V.V., et al.: Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets. Physica A 486, 206–217 (2017)

    Article  Google Scholar 

  44. López-Larraz, E., Montesano, L., Gil-Agudo, Á., Minguez, J., Oliviero, A.: Evolution of EEG motor rhythms after spinal cord injury: a longitudinal study. Plos One 10(7), e0131759 (2015)

    Article  Google Scholar 

  45. Gourab, K., Schmit, B.D.: Changes in movement-related beta-band EEG signals in human spinal cord injury. Clin. Neurophysiol. 121, 2017 (2010)

    Article  Google Scholar 

  46. Gao, L., Wang, J., Chen, L.: Event-related desynchronization and synchronization quantification in motor-related EEG by Kolmogorov entropy. J. Neural Eng. 10(3), 036023 (2013)

    Article  Google Scholar 

  47. Toro, C., Deuschl, G., Thatcher, R., Sato, S., Kufta, C., Hallett, M.: Event-related desynchronization and movement-related cortical potentials on the ECoG and EEG. Electroencephalogr. Clin. Neurophysiol. 93(5), 380–389 (1994)

    Article  Google Scholar 

  48. Duann, J.-R., Chiou, J.-C.: A comparison of independent event-related desynchronization responses in motor-related brain areas to movement execution, movement imagery, and movement observation. PLoS ONE 11(9), e0162546 (2016)

    Article  Google Scholar 

  49. Harmony, T.: The functional significance of delta oscillations in cognitive processing. Front. Integr. Neurosci. 7, 83 (2013)

    Article  Google Scholar 

  50. Donoghue, J.P., Sanes, J.N.: Motor areas of the cerebral cortex. J. Clin. Neurophysiol. 11(4), 382–396 (1994)

    Google Scholar 

  51. Todor, J.I., Doane, T.: Handedness and hemispheric asymmetry in the control of movements. J. Mot. Behav. 10(4), 295–300 (1978)

    Article  Google Scholar 

  52. Sharma, N., Pomeroy, V.M., Baron, J.-C.: Motor imagery: a backdoor to the motor system after stroke? Stroke 37, 19411952 (2006)

    Article  Google Scholar 

  53. Page, S.J., Szaflarski, J.P., Eliassen, J.C., Pan, H., Cramer, S.C.: Cortical plasticity following motor skill learning during mental practice in stroke. Neurorehabilit. Neural Repair 23, 382388 (2009)

    Google Scholar 

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Acknowledgements

This work was supported by the Russian Science Foundation (17-72-30003).

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Correspondence to Alexander E. Hramov.

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Maksimenko, V.A., Pavlov, A., Runnova, A.E. et al. Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects. Nonlinear Dyn 91, 2803–2817 (2018). https://doi.org/10.1007/s11071-018-4047-y

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  • DOI: https://doi.org/10.1007/s11071-018-4047-y

Keywords

  • Motor action
  • Motor imaginary
  • Wavelet analysis
  • Multifractal analysis
  • Event-related synchronization
  • Empirical mode decomposition
  • EEG
  • Hölder exponent