[Abstract + References] A Wireless BCI-FES Based on Motor Intent for Lower Limb Rehabilitation

Abstract

Recent investigations have proposed brain computer interfaces combined with functional electrical stimulation as a novel approach for upper limb motor recovery. These systems could detect motor intention movement as a power decrease of the sensorimotor rhythms in the electroencephalography signal, even in people with damaged brain cortex. However, these systems use a large number of electrodes and wired communication to be employed for gait rehabilitation. In this paper, the design and development of a wireless brain computer interface combined with functional electrical stimulation aimed at lower limb motor recovery is presented. The design requirements also account the dynamic of a rehabilitation therapy by allowing the therapist to adapt the system during the session. A preliminary evaluation of the system in a subject with right lower limb motor impairment due to multiple sclerosis was conducted and as a performance metric, the true positive rate was computed. The developed system evidenced a robust wireless communication and was able to detect lower limb motor intention. The mean of the performance metric was 75%. The results encouraged the possibility of testing the developed system in a gait rehabilitation clinical study.

References

  1. 1.
    Pfurtscheller, G., Mcfarland, D.: BCIs that use sensorimotor rhythms. In: Wolpaw, J.R., Wolpaw, E. (eds.) Brain-Computer Interfaces: Principles and Practice, pp. 227–240. Oxford University Press (2012)Google Scholar
  2. 2.
    Carrere, L.C., Tabernig, C.B.: Detection of foot motor imagery using the coefficient of determination for neurorehabilitation based on BCI technology. IFMBE Proc. 49, 944–947 (2015).  https://doi.org/10.1007/978-3-319-13117-7_239CrossRefGoogle Scholar
  3. 3.
    Sannelli, C., Vidaurre, C., Müller, K.R., Blankertz, B.: A large scale screening study with a SMR-based BCI: categorization of BCI users and differences in their SMR activity (2019)Google Scholar
  4. 4.
    Do, A.H., Wang, P.T., King, C.E., Schombs, A., Cramer, S.C., Nenadic, Z.: Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke, pp. 6414–6417 (2012)Google Scholar
  5. 5.
    Ramos-Murguialday, A., Broetz, D., Rea, M., Yilmaz, Ö., Brasil, F.L., Liberati, G., Marco, R., Garcia-cossio, E., Vyziotis, A., Cho, W., Cohen, L.G., Birbaumer, N.: Brain-Machine-interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108 (2014).  https://doi.org/10.1002/ana.23879.Brain-Machine-InterfaceCrossRefGoogle Scholar
  6. 6.
    Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., Schnider, A., Schmidlin, T., Zhang, H., Bassolino, M., Viceic, D., Vuadens, P., Guggisberg, A.G., Millán, J.D.R.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 9, 1–13 (2018).  https://doi.org/10.1038/s41467-018-04673-zCrossRefGoogle Scholar
  7. 7.
    Tabernig, C.B., Lopez, C.A., Carrere, L.C., Spaich, E.G., Ballario, C.H.: Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface. J. Rehabil. Assist. Technol. Eng. 5, 205566831878928 (2018).  https://doi.org/10.1177/2055668318789280CrossRefGoogle Scholar
  8. 8.
    McCrimmon, C.M., King, C.E., Wang, P.T., Cramer, S.C., Nenadic, Z., Do, A.H.: Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J. Neuroeng. Rehabil. 12 (2015).  https://doi.org/10.1186/s12984-015-0050-4
  9. 9.
    g.Nautilus wireless biosignal acquisition Homepage. http://www.gtec.at/Products/Hardware-and-Accessories/g.Nautilus-Specs-Features
  10. 10.
    Emotiv EpocFlex flexible wireless EEG system Homepage. https://www.emotiv.com/epoc-flex/
  11. 11.
    Vuckovic, A., Wallace, L., Allan, D.: Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. J. Neurol. Phys. Ther. 39, 3–14 (2015)CrossRefGoogle Scholar
  12. 12.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004).  https://doi.org/10.1109/TBME.2004.827072CrossRefGoogle Scholar
  13. 13.
    McCrimmon, C.M., Fu, J.L., Wang, M., Lopes, L.S., Wang, P.T., Karimi-Bidhendi, A., Liu, C.Y., Heydari, P., Nenadic, Z., Do, A.H.: Performance assessment of a custom, portable, and low-cost brain-computer interface platform. IEEE Trans. Biomed. Eng. 64, 2313–2320 (2017).  https://doi.org/10.1109/TBME.2017.2667579CrossRefGoogle Scholar

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