Skip to main content
Log in

Sources of the Electrical Activity of Brain Areas Involving in Imaginary Movements

  • Published:
Neuroscience and Behavioral Physiology Aims and scope Submit manuscript

We describe the most significant sources of electrophysiological brain activity identified during use of a brain–computer interface based on recognition of EEG patterns during imaginary movements. The main tool for identifying sources consisted of six independent components analysis (ICA) methods based on different criteria of independence. Measures of the significance of sources were: their occurrence rates in different experimental sessions; repetition of extraction in each session using different ICA methods; the effects of each source on of recognition accuracy for EEG patterns corresponding to different imaginary movements, and the potential for approximating the activity of an individual current dipole. The overall set of indicators identified five sources located in the primary somatosensory cortex of both hemispheres, in the left premotor area, the supplementary motor area, and the precuneus. The functional significance of these sources in the framework of the contemporary concepts of the interaction of brain areas supporting the execution of motor functions is discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Alivisatos, B. and Petrides, M., “Functional activation of human brain during mental rotation,” Neuropsychologia, 35, No. 2, 111–118 (1997).

    Article  CAS  PubMed  Google Scholar 

  • Altschuler, E. L., Vankov, A., Wang, V., et al., “Person see, person do: human cortical electrophysiological correlates of monkey see monkey do cells,” in: Poster Session Presented at the 27th Annual Meeting of the Society for Neuroscience, New Orleans, LA (1997).

  • Anderson, K. L. and Ding, M., “Attentional modulation of the somatosensory mu rhythm,” Neuroscience, 180, 165–180 (2011).

    Article  CAS  PubMed  Google Scholar 

  • Ang, K. K., Chua, K. S., Phua, K. S., et al., “A Randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke,” Clin. EEG Neurosci., 46, No. 4, 310–320 (2015).

    Article  PubMed  Google Scholar 

  • Bell, A. J. and Sejnowski, T. J., “An information-maximization approach to blind separation and blind deconvolution,” Neural Comput., 7, No. 6, 1129–1159 (1995).

    Article  CAS  PubMed  Google Scholar 

  • Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., and Moulines, E., “A blind source separation technique using second-order statistics,” IEEE Trans. Signal Proc., 45, No. 2, 434–444 (1997).

    Article  Google Scholar 

  • Binkofski, F., Amunts, K., Stephan, K. M., et al., “Broca’s region subserves imagery of motion: a combined cytoarchitectonic and fMRI study,” Hum. Brain Mapp., 11, No. 4, 273–285 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Blankertz, B., Dornhege, G., Krauledat, M., et al., “The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects,” NeuroImage, 37, No. 2, 539–550 (2007).

    Article  PubMed  Google Scholar 

  • Bobrov, P. D., Korshakov, A. V., Roshchin, V. Yu., and Frolov, A. A., “A Bayesian approach to realizing a brain-computer interface based on imaginary movements,” Zh. Vyssh. Nerv. Deyat., 6, No. 1, 89–99 (2012).

    Google Scholar 

  • Bobrov, P., Frolov, A., Cantor, C., et al., “Brain-computer interface based on generation of visual images,” PLoS One, 6, No. 6, e20674 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bobrov, P., Frolov, A., Husek, D., Snášel, V., “Clustering the sources of EEG activity during motor imagery by attractor neural network with increasing activity (ANNIA),” in: Proc. 5th Int. Conf. on Innovations in Bio-Inspired Computing and Applications IBICA, Springer, Champagne, (2014), pp. 183–191.

  • Catalan, M. J., Honda, M., Weeks, R. A., et al., “The functional neuroanatomy of simple and complex sequential finger movements: a PET study,” Brain, 121, No. 2, 253–264 (1998).

    Article  PubMed  Google Scholar 

  • Cavanna, A. E. and Trimble, M. R., “The precuneus: a review of its functional anatomy and behavioural correlates,” Brain, 129, No. 3, 564–583 (2006).

    Article  PubMed  Google Scholar 

  • Cervera, M. A., Soekadar, S. R., Ushiba, J., et al., “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis,” Ann. Clin. Transl. Neurol., 5, No. 5, 651–663 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  • Christensen, M. S., Lundbye-Jensen, J., Geertsen, S. S., et al., “Premotor cortex modulates somatosensory cortex during voluntary movements without proprioceptive feedback,” Neuroscientist, 10, No. 4, 417–419 (2007).

    CAS  Google Scholar 

  • Cochin, S., Barthelemy, C., Roux, S., and Martineau, J., “Observation and execution of movement: similarities demonstrated by quantified electroencephalography,” Eur. J. Neurosci., 11, No. 5, 1839–1842 (1999).

    Article  CAS  PubMed  Google Scholar 

  • Delorme, A., Palmer, J., Onton, J., et al., “Independent EEG sources are dipolar,” PLoS One, 7, No. 2, e30135 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dong, Y., Fukuyama, H., Honda, M., et al., “Essential role of right superior parietal cortex in Japanse kana mirror reading,” Brain, 123, No. 4, 790–799 (2000).

    Article  PubMed  Google Scholar 

  • Ehrsson, H. H., Geyer, S., and Naito, E., “Imagery of voluntary movement of fingers, toes and tongue activates corresponding body-part-specific motor representations,” J. Neurophysiol., 90, No. 5, 3304–3316 (2003).

    Article  PubMed  Google Scholar 

  • Fadiga, L., Buccino, G., Craighero, L., et al., “Corticospinal excitability is specifically modulated by motor imagery: a magnetic stimulation study,” Neuropsychologia, 37, No. 2, 147–158 (1999).

    Article  CAS  PubMed  Google Scholar 

  • Francuz, P. and Zapata, D., “The suppression of the ÎĽ rhythm during the creation of imagery representation of movement,” Neurosci. Lett., 495, No. 1, 39–43 (2011).

    Article  CAS  PubMed  Google Scholar 

  • Frolov, A. A., Aziatskaya, G. A., Bobrov, P. D., et al., “Electrophysiological activity of the brain in controlling a brain-computer interface based on imaginary movements,” Fiziol. Cheloveka, 43, No. 5, 17–28 (2017b).

    Google Scholar 

  • Frolov, A. A., Fedotova, I. R., Husek, D., and Bobrov, P. D., “Rhythmic brain activity and a brain-computer interface based on imaginary movements,” Usp. Fiziol. Nauk., 48, No. 3, 72–91 (2017a).

    Google Scholar 

  • Frolov, A. A., Husek, D., and Polyakov, P. Y., “Recurrent-neural-networkbased Boolean factor analysis and its application to word clustering,” IEEE Trans. Neural Netw., 20, No. 7, 1073 (2009).

    Article  PubMed  Google Scholar 

  • Frolov, A. A., Mokienko, O., Lyukmanov, R., et al., “Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: a randomized controlled multicenter trial,” Front. Neurosci., 11, 400 (2017).

  • Frolov, A., Husek, D., and Bobrov, P., “Comparison of four classification methods for brain–computer interface,” Neural Network World, 21, No. 2, 101–115 (2011).

    Article  Google Scholar 

  • Frolov, A., Husek, D., Bobrov, P., et al., “Sources of EEG activity most relevant to performance of brain–computer interface based on motor imagery,” Neural Network World, 22, No. 1, 21–37 (2012).

    Article  Google Scholar 

  • Gerardin, E., Sirigu, A., Lehericy, S., et al., “Partially overlapping neural networks for real and imagined hand movements,” Cereb. Cortex, 10, No. 11, 1093–1104 (2000).

    Article  CAS  PubMed  Google Scholar 

  • Grech, R., Cassar, T., Muscat, J., et al., “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil., 5, Art. 25, 1–33 (2008).

  • Grezes, J. and Decety, J., “Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta-analysis,” Hum. Brain Mapp., 12, No. 1, 1–19 (2001).

    Article  CAS  PubMed  Google Scholar 

  • Guillot, A., Collet, C., Nguyen, V. A., et al., “Brain activity during visual versus kinesthetic imagery: An fMRI study,” Hum. Brain Mapp., 30, No. 7, 2157–2172 (2009).

    Article  PubMed  Google Scholar 

  • Guillot, A., Di Rienzo, F., and Collet, C., “The neurofunctional architecture of motor imagery,” in: Advanced Brain Neuroimaging Topics in Health and Disease – Methods and Applications, IntechOpen (2014).

  • Hanakawa, T., Immisch, I., Toma, K., et al., “Functional properties of brain areas associated with motor execution and imagery,” J. Neurophysiol., 89, No. 2, 989–1002 (2003).

    Article  PubMed  Google Scholar 

  • Hashimoto, R. and Rothwell, J. C., “Dynamic changes in corticospinal excitability during motor imagery,” Exp. Brain Res., 125, No. 1, 75–81 (1999).

    Article  CAS  PubMed  Google Scholar 

  • Hetu, S., Gregoire, M., Saimpont, A., et al., “The neural network of motor imagery: an ALE meta-analysis,” Neurosci. Biobehav. Rev., 37, No. 5, 930–949 (2013).

    Article  PubMed  Google Scholar 

  • Hughes, S. W. and Crunelli, V., “Thalamic mechanisms of EEG alpha rhythms and their pathological implications,” Neuroscientist, 11, No. 4, 357–372 (2005).

    Article  PubMed  Google Scholar 

  • Hyvarinen, A., Karhunen, J., and Oje, E., Independent Component Analysis, Wiley, New York (2001).

    Book  Google Scholar 

  • Jones, S. R., Kerr, C. E., Wan, Q., et al., “Cued spatial attention drives functionally relevant modulation of the mu rhythm in primary somatosensory cortex,” J. Neurosci., 30, No. 41, 13760–13775 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jones, S. R., Pritchett, D. L., Sikora, M. A., et al., “Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm: rhythmogenesis and modulation of sensory-evoked responses,” J. Neurophysiol., 102, No. 6, 3554–3572 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  • Kachenoura, A., Albera, L., Senhadji, L., and Comon, P., “ICA: a potential tool for BCI systems,” IEEE Signal Process. Mag., 25, No. 1, 57–68 (2008).

    Article  Google Scholar 

  • Kasess, C. H., Windischberger, C., Cunnington, R., et al., “The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling,” NeuroImage, 40, No. 2, 828–837 (2008).

    Article  PubMed  Google Scholar 

  • Klimesch, W., “Alpha-band oscillations, attention, and controlled access to stored information,” Trends Cogn. Sci., 16, No. 12, 606–617 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  • Kohavi, R. and Provost, F., “Glossary of terms. Special issue on applications of machine learning and the knowledge discovery process,” Machine Learning, 30, 271–274 (1998).

    Article  Google Scholar 

  • Lotze, L., Montoya, P., Erb, M., et al., “Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study,” J. Cogn. Neurosci., 11, No. 5, 491–501 (1999).

    Article  CAS  PubMed  Google Scholar 

  • Malouin, F., Richards, C. L., Jackson, P. L., et al., “Brain activations during motor imagery of locomotor-related tasks: a PET study,” Hum. Brain Mapp., 19, No. 1, 47–62 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  • McFarland, D. J., Miner, L. A., Vaughan, T. M., and Wolpaw, J. R., “Mu and beta rhythm topographies during motor imagery and actual movements,” Brain Topogr., 12, No. 3, 177–186 (2000).

    Article  CAS  PubMed  Google Scholar 

  • Mokienko, O., Chervyakov, A., Kulikova, S., et al., “Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects,” Front. Comput. Neurosci., 7, No. 168) (2013).

  • Muler, C. and Lemieux, L., EEG-fMRI. Physiological Basis, Techniques and Application, Springer, Berlin (2010).

  • Nair, D. G., Purcott, K. L., Fuchs, A., et al., “Cortical and cerebellar activity of the human brain during imagined and executed unimanual and bimanual action sequences: a functional MRI study,” Cogn. Brain Res., 15, No. 3, 250–260 (2003).

    Article  Google Scholar 

  • Nam, C. S., Jeon, Y., Kim, Y. J., et al., “Movement imagery-related lateralization of event-related (de) synchronization (ERD/ERS), motor-imagery duration effects,” Clin. Neurophysiol., 122, No. 3, 567–77 (2011).

    Article  PubMed  Google Scholar 

  • Onton, J., Westerfield, M., Townsend, J., and Makeig, S., “Imaging human EEG dynamics using independent component analysis,” Neurosci. Biobehav. Rev., 30, No. 6, 808–822 (2006).

    Article  PubMed  Google Scholar 

  • Palmer, J. A., Kreutz-Delgado, K., and Makeig, S., AMICA: An Adaptive Mixture of Independent Component Analyzers with Shared Components, Technical Report, Swartz Center for Comput. Neuroscience, San Diego, CA (2011).

  • Penna, S. D., Torquati, K., Pizzella, V., et al., “Temporal dynamics of alpha and beta rhythms in human SI and SII after galvanic median nerve stimulation. A MEG study,” NeuroImage, 22, No. 4, 1438–1446 (2004).

    Article  PubMed  Google Scholar 

  • Pfurtscheller, G., “Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest,” Electroencephalogr. Clin. Neurophysiol., 83, No. 1, 62–69 (1992).

    Article  CAS  PubMed  Google Scholar 

  • Pfurtscheller, G. and Lopes da Silva, F. H., “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clin. Neurophysiol., 110, No. 11, 1842–1857 (1999).

    Article  CAS  PubMed  Google Scholar 

  • Pfurtscheller, G., Brunner, C., Schlogl, A., and Lopes da Silva, F. H., “Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks,” NeuroImage, 31, No. 1, 153–159 (2006).

    Article  CAS  PubMed  Google Scholar 

  • Porro, C. A., Cettolo, V., Francescato, M. P., and Baraldi, P., “Ipsilateral involvement of primary motor cortex during motor imagery,” Eur. J. Neurosci., 12, No. 8, 3059–3063 (2000).

    Article  CAS  PubMed  Google Scholar 

  • Rizzolatti, G. and Craighero, L., “The mirror-neuron system,” Ann. Rev. Neurosci., 27, 169–192 (2004).

    Article  CAS  PubMed  Google Scholar 

  • Rizzolatti, G., Cattaneo, L., Fabbri-Destro, M., and Rozzi, S., “Cortical mechanisms underlying the organization of goal-detected actionsand mirror neuron based action understanding,” Physiol. Rev., 94, No. 2, 655–706 (2014).

    Article  PubMed  Google Scholar 

  • Sitaram, R., Ros, T., Stoeckel, L., et al., “Closed-loop brain training: the science of neurofeedback,” Nat. Rev. Neurosci., 18, No. 2, 86–100 (2016).

    Article  PubMed  CAS  Google Scholar 

  • Solodkin, A., Hlustik, P., Chen, E. E., and Small, S. L., “Fine modulation in network activation during motor execution and motor imagery,” Cereb. Cortex, 14, No. 11, 1246–1255 (2004).

    Article  PubMed  Google Scholar 

  • Stinear, C. M., “Corticospinal facilitation during motor imagery,” in: The Neuro-Physiological Foundations of Mental and Motor Imagery, Guillot, A. and Collet, C. (eds.), Oxford University Press (2010), pp. 47–61.

  • Sun, H., Blakely, T. M., Darvas, F., et al., “Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued finger movements,” Clin. Neurophysiol., 126, No. 11, 2150–2161 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  • Vasil’ev, A. N., Liburkina, S. P., and Kaplan, A. Ya., “Lateralization of EEG patterns in humans in imaginary hand movements in a brain–computer interface,” Zh. Vyssh. Nerv. Deyat., 66, No. 3, 302–312 (2016).

    Google Scholar 

  • Wang, W., Collinger, J. L., Degenhar, A. D., et al., “An electrocorticographic brain interface in an individual with tetraplegia,” PLoS One, 8, No. 2, e55344 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. A. Frolov.

Additional information

Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 69, No. 6, pp. 711–725, November–December, 2019.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kerechanin, Y.V., Husek, D., Bobrov, P.D. et al. Sources of the Electrical Activity of Brain Areas Involving in Imaginary Movements. Neurosci Behav Physi 50, 845–855 (2020). https://doi.org/10.1007/s11055-020-00977-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11055-020-00977-0

Keywords

Navigation