Skip to main content
Log in

Analysis of Cerebral and Muscle Activity during Control of a Corticospinal Neural Interface

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

A corticospinal neural interface was developed on the basis of kinesthetic imagery of dorsiflexion of the foot complemented by the Biokin robotic limb movement device and transcutaneous electrical stimulation of the spinal cord (TESSC). The classification accuracy (CA – the proportion of correct responses) of EEG brain signals while working with the neural interface was found to average 68% and to increase significantly, by 9%, on addition of mechanotherapy and TESSC. The EMG activity of the tibialis anterior muscle (TA), which dorsiflexes the foot, increased when subjects were instructed to imagine movement as compared with the activity when the instruction was to remain at rest. Addition of mechanotherapy and TESSC when using the neural interface had a greater effect not by increasing TA activity when imagining movement of the ipsilateral foot, but by reducing TA activity when instructed to remain at rest. The effects identified here appear to be important for forming appropriate coordination patterns of control signals from the central nervous system and muscle activity during performance of movements and can be used in clinical rehabilitation of motor activity using the corticospinal neural interface.

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

  • Alam, M., Rodrigues, W., Pham, B. N., and Thakor, N. V., “Brain–machine interface facilitated neurorehabilitation via spinal stimulation after spinal cord injury: recent progress and future perspectives,” Brain Res., 1646, 25–33 (2016).

    Article  CAS  PubMed  Google Scholar 

  • Bai, Z., Fong, K. N. K., Zhang, J. J., et al., “Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis,” J. Neuroeng. Rehabil., 17, No. 1, 57 (2020).

  • Bentley, L. D., Duarte, R. V., Furlong, P. L., et al., “Brain activity modifications following spinal cord stimulation for chronic neuropathic pain: a systematic review,” Eur. J. Pain, 20, 499–511 (2016).

    Article  CAS  PubMed  Google Scholar 

  • Bobrova, E. V., Bogacheva, I. N., Lyakhovetskii, V. A., et al., “Memorization of sequences of movements of the right and left hand by right- and left-handers,” Human Physiol., 41, 629–635 (2015).

    Article  Google Scholar 

  • Bobrova, E. V., Bogacheva, I. N., Lyakhovetskii, V. A., et al., “Memorization of sequences of movements of the right or the left hand by right- and left-handers: vector coding,” Human Physiol., 43, 13–21 (2017).

    Article  Google Scholar 

  • Bobrova, E. V., Lyakhovetskii, V. A., and Borshchevskaya, E. R., “The role of ‘prehistory’ in the reproduction of sequential movements of the right and left hands: encoding of positions, movements, and sequence structure,” Neurosci. Behav. Physiol., 43, 56–62 (2013).

    Article  Google Scholar 

  • Bobrova, E. V., Reshetnikova, V. V., Frolov, A. A., and Gerasimenko, Yu. P., “Lower limb motor imagery for control of brain–computer interface systems,” Zh. Vyssh. Nerv. Deyat., 69, No. 5, 529–540 (2019).

    Google Scholar 

  • Bobrova, E. V., Reshetnikova, V. V., Vershinina, E. A., et al., “Assessment of the effectiveness of brain–computer interface control in teaching upper and lower limb motor imagery,” Zh. Vyssh. Nerv. Deyat., 73, No. 1, 52–61 (2022).

    Google Scholar 

  • Bodrova, R. A., “Mechanotherapy with biofeedback: effective rehabilitation for spinal cord injury,” Doktor.Ru, 10, No. 78, 46–47 (2012).

  • Bogacheva, I. N., Moshonkina, T. R., Bobrova, E. V., et al., “The effect of transcutaneous electrical stimulation of the spinal cord and mechanotherapy in the regulation of leg muscle activity,” Vestn. TvGU Ser. Biol. Ekol., 2, 7–17 (2015).

    Google Scholar 

  • Bonizzato, M., Pidpruzhnykova, G., DiGiovanna, J., et al., “Brain-controlled modulation of spinal circuits improves recovery from spinal cord injury,” Nat. Commun., 9, 1–14 (2018).

    Article  CAS  Google Scholar 

  • Bouton, C. E., “Chapter 22 – Merging brain–computer interface and functional electrical stimulation technologies for movement restoration,” in: Handbook of Clinical Neurology, Ramsey, N. F. and Millán, J. del R. (eds.), Elsevier (2020), Vol. 168, pp. 303–309.

  • Burianová, H., Marstaller, L., Rich, A. N., et al., “Motor neuroplasticity: A MEG-fMRI study of motor imagery and execution in healthy ageing,” Neuropsychologia, 146, 107539 (2020).

    Article  PubMed  Google Scholar 

  • Camargo-Vargas, D., Callejas-Cuervo, M., and Mazzoleni, S., “Brain– computer interfaces systems for upper and lower limb rehabilitation: a systematic review,” Sensors, 21, No. 13, 4312 (2021).

    Google Scholar 

  • Capogrosso, M., Milekovic, T., Borton, D., et al., “A brain–spine interface alleviating gait deficits after spinal cord injury in primates,” Nature, 539, 284–288 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  • Cardoso, V. F., Delisle-Rodriguez, D., Romero-Laiseca, M. A., et al., “Effect of a brain–computer interface based on pedaling motor imagery on cortical excitability and connectivity,” Sensors, 21, No. 6, 2020 (2021).

    Google Scholar 

  • Cheron, G., Duvinage, M., De Saedeleer, C., et al., “From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation,” Neural Plast., 2012, 375148 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cui, Z., Li, Y., Huang, S., et al., “BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study,” Cogn. Neurodyn., 16, 1283–1301 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  • Daminov, V. D., “Robotic mechanotherapy in neurorehabilitation,” Vest. AGIUV, 3, 83–88 (2013).

    Google Scholar 

  • De Ridder, D., Plazier, M., Kamerling, N., et al., “Burst spinal cord stimulation for limb and back pain,” World Neurosurg., 80, 642–649 (2013).

    Article  PubMed  Google Scholar 

  • Dickstein, R., Gazit-Grunwald, M., Plax, M., et al., “EMG activity in selected target muscles during imagery rising on tiptoes in healthy adults and poststroke hemiparetic patients,” J. Mot. Behav., 37, No. 6, 475–483 (2005).

    Article  PubMed  Google Scholar 

  • Do, A. H., Wang, P. T., King, C. E., et al., “Brain–computer interface controlled functional electrical stimulation system for ankle movement,” J. Neuroeng. Rehabil., 8,49 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  • Do, A. H., Wang, P. T., King, C. E., et al., “Brain–computer interface controlled robotic gait orthosis,” J. Neuroeng. Rehabil., 10, 111 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  • Donati, A., Shokur, S., Morya, E., et al., “Long-term training with a brain–machine interface-based gait protocol induces partial neurological recovery in paraplegic patients,” Sci. Rep., 6, 30383 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Frolov, A. A. and Bobrov, P. D., “Brain–computer interface: Neurophysiological bases and clinical applications,” Zh. Vyssh. Nerv. Deyat., 67, No. 4, 365–376 (2017).

    Google Scholar 

  • Gandevia, S. C., Wilson, L. R., Inglis, J. T., and Burke, D., “Mental rehearsal of motor tasks recruits α-motoneurones but fails to recruit human fusimotor neurones selectively,” J. Physiol., 505, No. 1, 259–266 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gao, W., Cui, Z., Yu, Y., et al., “Application of a brain–computer interface system with visual and motor feedback in limb and brain functional rehabilitation after stroke: case report,” Brain Sci., 12, No. 8, 1083 (2022).

  • García-Cossio, E., Severens, M., Nienhuis, B., et al., “Decoding sensorimotor rhythms during robotic-assisted treadmill walking for brain computer interface (BCI) applications,” PLoS One, 10, No. 12, e0137910 (2015).

    Google Scholar 

  • Geiger, D. E., Behrendt, F., and Schuster-Amft, C., “EMG muscle activation pattern of four lower extremity muscles during stair climbing, motor imagery, and robot-assisted stepping: a cross-sectional study in healthy individuals,” BioMed. Res. Int., 2019, 9351689 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  • Gerasimenko, Y., Gorodnichev, R., Puhov, A., et al., “Initiation and modulation of locomotor circuitry output with multisite transcutaneous electrical stimulation of the spinal cord in noninjured humans,” J. Neurophysiol., 113, No. 3, 834–842 (2015).

    Article  PubMed  Google Scholar 

  • Gorodnichev, R. M., Pivovarova, E. A., Pukhov, A., et al., “Transcutaneous electrical stimulation of the spinal cord: a non-invasive method for activating stepping motion generators in humans,” Fiziol. Cheloveka, 38, No. 2, 46–56 (2012).

    CAS  PubMed  Google Scholar 

  • Grishin, A. A., Moshonkina, T. R., Bobrova, E. V., and Gerasimenko, Yu. P., “A device for the rehabilitation therapy of patients with motor pathology using mechanotherapy, transcutaneous electrical stimulation of the spinal cord, and biological feedback,” Biomed. Eng., 53, 227–230 (2019).

    Article  Google Scholar 

  • Grosprêtre, S., Lebon, F., Papaxanthis, C., and Martin, A., “New evidence of corticospinal network modulation induced by motor imagery,” J. Neurophysiol., 115, No. 3, 1279–1288 (2016).

    Article  PubMed  Google Scholar 

  • Guillot, A., Lebon, F., Rouffet, D., et al., “Muscular responses during motor imagery as a function of muscle contraction types,” Int. J. Psychophysiol., 66, No. 1, 18–27 (2007).

    Article  CAS  PubMed  Google Scholar 

  • Haaland, K. Y., “Hemispheric asymmetries for kinematic and positional aspects of reaching,” Brain, 127, 1145–1158 (2004).

    Article  PubMed  Google Scholar 

  • Harkema, S., Gerasimenko, Y., Hodes, J., et al., “Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study,” Lancet, 377, No. 9781, 1938–1947 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  • Harrington, D. L. and Haaland, K. Y., “Hemispheric specialization for motor sequencing: Abnormalities in levels of programming,” Neuropsychologia, 29, 147–163 (1991).

    Article  CAS  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 

  • Hramov, A. E., Maksimenko, V. A., and Pisarchik, A. N., “Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states,” Phys. Rep., 918, 1–133 (2021).

    Article  Google Scholar 

  • Insausti-Delgado, A., López-Larraz, E., Nishimura, Y., et al., “Noninvasive brain–spine interface: continuous control of trans-spinal magnetic stimulation using EEG,” Front. Bioeng. Biotechnol., 10, 975037 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  • Jayaram, V. and Barachant, A., “MOABB: trustworthy algorithm benchmarking for BCIs,” J. Neural Eng., 15, No. 6, 066011 (2018).

  • Kaneko, F., Hayami, T., Aoyama, T., and Kizuka, T., “Motor imagery and electrical stimulation reproduce corticospinal excitability at levels similar to voluntary muscle contraction,” J. Neuroeng. Rehabil., 11, 94 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  • King, C. E., Wang, P. T., Chui, L. A., et al., “Operation of a brain–computer interface walking simulator for individuals with spinal cord injury,” J. Neuroeng. Rehabil., 10,77 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  • King, C. E., Wang, P. T., McCrimmon, C. M., et al., “Brain–computer interface driven functional electrical stimulation system for overground walking in spinal cord injury participant,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2014, 1238–1242 (2014).

    Google Scholar 

  • King, C. E., Wang, P. T., McCrimmon, C. M., et al., “The feasibility of a brain–computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia,” J. Neuroeng. Rehabil., 12, 80 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  • Kruse, A., Suica, Z., Taeymans, J., and Schuster-Amft, C., “Effect of brain–computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke-a systematic review and meta-analysis,” BMC Neurol., 20, No. 1, 1–14 (2020).

    Article  Google Scholar 

  • Li, C., Wei, J., Huang, X., et al., “Effects of a brain–computer interface-operated lower limb rehabilitation robot on motor function recovery in patients with stroke,” J. Healthc. Eng., 2021, 4710044 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  • Lotte, F., Bougrain, L., Cichocki, A., et al., “A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update,” J. Neural Eng., 15, No. 3, 031005 (2018).

  • Luu, T. P., He, Y., Brown, S., et al., “Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar,” J. Neural Eng., 13, 036006 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  • Manson, G. A., Atkinson, D. A., Shi, Z., et al., “Transcutaneous spinal stimulation alters cortical and subcortical activation patterns during mimicked-standing: A proof-of-concept fMRI study,” Neuroimage Rep., 2, No. 2, 100090 (2022).

  • McGeady, C., Vučković, A., Zheng, Y.-P., and Alam, M., “EEG monitoring is feasible and reliable during simultaneous transcutaneous electrical spinal cord stimulation,” Sensors, 21, No. 19, 6593 (2021).

    Google Scholar 

  • McPherson, J. G., Miller, R. R., Perlmutter, S. I., et al., “Targeted, activity- dependent spinal stimulation produces long-lasting motor recovery in chronic cervical spinal cord injury,” Proc. Natl. Acad. Sci. USA, 78, 12193–12198 (2015).

    Article  Google Scholar 

  • Moens, M., Sunaert, S., Mariën, P., et al., “Spinal cord stimulation modulates cerebral function: an fMRI study,” Neuroradiology, 54, No. 12, 1399–1407 (2012).

    Article  CAS  PubMed  Google Scholar 

  • Mrachacz-Kersting, N., Jiang, N., Stevenson, A. J., et al., “Efficient neuroplasticity induction in chronic stroke patients by an associative brain–computer interface,” J. Neurophysiol., 115, No. 3, 1410–21 (2016).

    Article  PubMed  Google Scholar 

  • Mulder, T., De Vries, S., and Zijlstra, S., “Observation, imagination and execution of an effortful movement: more evidence for a central explanation of motor imagery,” Exp. Brain Res., 163, No. 3, 344–351 (2005).

    Article  PubMed  Google Scholar 

  • Nishimura, Y., Perlmutter, S. I., and Fetz, E. E., “Restoration of upper limb movement via artificial corticospinal and musculospinal connections in a monkey with spinal cord injury,” Front. Neural Circuits, 7, 57 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  • Page, S. J., “An overview of the effectiveness of motor imagery after stroke: a neuroimaging approach,” in: The Neurophysiological Foundations of Mental and Motor Imagery, Guillot, A. and Collet, C. (eds.), Oxford Academic Press, Oxford (2012), pp. 145–160.

    Google Scholar 

  • Pérez, M. Q., Beltrán, E. T. M., Bernal, S. L., et al., “Breaching subjects’ thoughts privacy: a study with visual stimuli and brain–computer interfaces,” J. Healthc. Eng., 2021, 5517637 (2021).

    Google Scholar 

  • Personnier, P., Paizis, C., Ballay, Y., and Papaxanthis, C., “Mentally represented motor actions in normal aging. II. The influence of the gravito-inertial context on the duration of overt and covert arm movements,” Behav. Brain Res., 186, No. 2, 273–283 (2008).

    Article  PubMed  Google Scholar 

  • Pino, A., Tovar, N., Barria, P., et al., “Brain–computer interface for controlling lower-limb exoskeletons,” in: Interfacing Humans and Robots for Gait Assistance and Rehabilitation, Springer, Chamoaign (2022), pp. 237–258.

  • Posner, M. I. and Rothbart, M. K., “Research on attention networks as a model for the integration of psychological science,” Annu. Rev. Psychol., 58, 1–23 (2007).

    Article  PubMed  Google Scholar 

  • Ranganathan, V. K., Siemionow, V., Liu, J. Z., et al., “From mental power to muscle power–gaining strength by using the mind,” Neuropsychologia, 42, No. 7, 944–956 (2004).

    Article  PubMed  Google Scholar 

  • Ren, S., Wang, W., Hou, Z.-G., et al., “Enhanced motor imagery based brain–computer interface via FES and VR for lower limb,” IEEE Trans. Neural Syst., 28, No. 8, 1846–1855 (2020).

    Article  Google Scholar 

  • Saha, S., Mamun, K. A., et al., “Progress in brain computer interface: challenges and potentials,” Front. Syst. Neurosci., 15, 4 (2021).

    Article  Google Scholar 

  • Sainburg, R. L. and Duff, S. V., “Does motor lateralization have implications for stroke rehabilitation?” J. Rehabil. Res. Dev, 43, 311 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  • Sainburg, R. L., “Handedness: differential specializations for control of trajectory and position,” Exerc. Sport Sci. Rev., 33, 206–213 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  • Steele, A. G., Manson, G. A., Horner, P. J., et al., “Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: a proof-of-concept EEG study,” J. Neural Eng., 19, No. 4, 046001 (2022).

  • Stolbkov, Yu. K., Moshonkina, T. R., Orlov, I. V., et al., “Motor imagery as a means of improving and rehabilitating motor functions,” Usp. Fiziol. Nauk., 49, No. 2, 45–59 (2018).

    Google Scholar 

  • Takahashi, M., Takeda, K., Otaka, Y., et al., “Event related desynchronization- modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study,” J. Neuroeng. Rehabil., 9, 56 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  • Takahashi, Y., Kawakami, M., et al., “Effects of leg motor imagery combined with electrical stimulation on plasticity of corticospinal excitability and spinal reciprocal inhibition,” Front. Neurosci., 13, 149 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  • Telkes, L., Hancu, M., Paniccioli, S., et al., “Differences in EEG patterns between tonic and high frequency spinal cord stimulation in chronic pain patients,” Clin. Neurophysiol., 131, No. 8, 1731–1740 (2020).

    Article  PubMed  Google Scholar 

  • Yadav, A. P., Li, D., and Nicolelis, M. A. L., “A brain to spine interface for transferring artificial sensory information,” Sci. Rep., 10, 900–915 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yasui, T., Yamaguchi, T., Tanabe, S., et al., “Time course of changes in corticospinal excitability induced by motor imagery during action observation combined with peripheral nerve electrical stimulation,” Exp. Brain Res., 237, 637–645 (2019).

    Article  PubMed  Google Scholar 

  • Zimmermann, J. B. and Jackson, A., “Closed-loop control of spinal cord stimulation to restore hand function after paralysis,” Front. Neurosci., 8, 87–88 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. V. Bobrova.

Additional information

Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 73, No. 4, pp. 510–523, July–August, 2023.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bobrova, E.V., Reshetnikova, V.V., Grishin, A.A. et al. Analysis of Cerebral and Muscle Activity during Control of a Corticospinal Neural Interface. Neurosci Behav Physi 53, 1574–1583 (2023). https://doi.org/10.1007/s11055-023-01552-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11055-023-01552-z

Keywords

Navigation