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Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation

  • Octavio Marin-Pardo
  • Athanasios Vourvopoulos
  • Meghan Neureither
  • David Saldana
  • Esther Jahng
  • Sook-Lei LiewEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1032)

Abstract

Virtual reality (VR)-based biofeedback of brain signals using electroencephalography (EEG) has been utilized to encourage the recovery of brain-to-muscle pathways following a stroke. Such models incorporate principles of action observation with neurofeedback of motor-related brain activity to increase sensorimotor activity on the lesioned hemisphere. However, for individuals with existing muscle activity in the hemiparetic arm, we hypothesize that providing biofeedback of muscle signals, to strengthen already established brain-to-muscle pathways, may be more effective. In this project, we aimed to understand whether and when feedback of muscle activity (measured using surface electromyography (EMG)) might more effective compared to EEG biofeedback. To do so, we used a virtual reality (VR) training paradigm we developed for stroke rehabilitation (REINVENT), which provides EEG biofeedback of ipsilesional sensorimotor brain activity and simultaneously records EMG signals. We acquired 640 trials over eight 1.5-h sessions in four stroke participants with varying levels of motor impairment. For each trial, participants attempted to move their affected arm. Successful trials, defined as when their EEG sensorimotor desynchronization (8–24 Hz) during a time-limited movement attempt exceeded their baseline activity, drove a virtual arm towards a target. Here, EMG signals were analyzed offline to see (1) whether EMG amplitude could be significantly differentiated between active trials compared to baseline, and (2) whether using EMG would have led to more successful VR biofeedback control than EEG. Our current results show a significant increase in EMG amplitude across all four participants for active versus baseline trials, suggesting that EMG biofeedback is feasible for stroke participants across a range of impairments. However, we observed significantly better performance with EMG than EEG for only the three individuals with higher motor abilities, suggesting that EMG biofeedback may be best suited for those with better motor abilities.

Keywords

Human-computer interfaces Stroke rehabilitation Electromyography Biofeedback Virtual reality 

Notes

Acknowledgments

This research was supported by the American Heart Association through the REINVENT project (Grant #16IRG26960017) and a USC-CONACyT fellowship jointly given by the University of Southern California and the Mexican National Council of Science and Technology.

References

  1. 1.
    Langhorne, P., Bernhardt, J., Kwakkel, G.: Stroke rehabilitation. Lancet 377, 1693–1702 (2011).  https://doi.org/10.1016/S0140-6736(11)60325-5CrossRefGoogle Scholar
  2. 2.
    Ramos-Murguialday, A., et al.: Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. (2013).  https://doi.org/10.1002/ana.23879CrossRefGoogle Scholar
  3. 3.
    Shindo, K.: Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J. Rehabil. Med. 43, 951–957 (2016).  https://doi.org/10.2340/16501977-0859CrossRefGoogle Scholar
  4. 4.
    Armagan, O., Tascioglu, F., Oner, C.: Electromyographic biofeedback in the treatment of the hemiplegic hand: a placebo-controlled study. Am. J. Phys. Med. Rehabil. 82, 856–861 (2003).  https://doi.org/10.1097/01.PHM.0000091984.72486.E0CrossRefGoogle Scholar
  5. 5.
    Garrison, K.A., Aziz-Zadeh, L., Wong, S.W., Liew, S.L., Winstein, C.J.: Modulating the motor system by action observation after stroke. Stroke 44, 2247–2253 (2013).  https://doi.org/10.1161/STROKEAHA.113.001105CrossRefGoogle Scholar
  6. 6.
    Celnik, P., Webster, B., Glasser, D.M., Cohen, L.G.: Effects of action observation on physical training after stroke. Stroke. 39, 1814–1820 (2008).  https://doi.org/10.1161/STROKEAHA.107.508184CrossRefGoogle Scholar
  7. 7.
    Vourvopoulos, A., Bermúdezi Badia, S.: Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J. Neuroeng. Rehabil. 13, 1–14 (2016).  https://doi.org/10.1186/s12984-016-0173-2CrossRefGoogle Scholar
  8. 8.
    Spicer, R., Anglin, J., Krum, D.M., Liew, S.L.: REINVENT: a low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery. In: Proceedings of IEEE Virtual Reality, pp. 385–386 (2017).  https://doi.org/10.1109/vr.2017.7892338
  9. 9.
    Klem, G.H., Lüders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol. 10, 371–375 (1999).  https://doi.org/10.1016/0013-4694(58)90053-1CrossRefGoogle Scholar
  10. 10.
    Kothe, C.: Lab Streaming Layer (LSL). https://github.com/sccn/labstreaminglayer
  11. 11.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9–21 (2004).  https://doi.org/10.1016/j.jneumeth.2003.10.009CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Southern CaliforniaLos AngelesUSA

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