Abstract
Brain–computer interface (BCI) is an emerging tool that has a variety of practical applications, including rehabilitation. BCIs are systems that extract and classify features in neural data, and then produce an output when a specific feature is detected. Motor imagery-based BCIs (MI BCIs), a more specific form of BCI, detect features that indicate the user is imagining a specific motor action, such as moving their arm or leg. There have been several studies released discussing the potential for BCIs to be used in a clinical setting for applications like rehabilitation. Spinal cord injuries (SCIs) are a form of injury that damages the spinal cord and causes either partial or total paralyzation. Those with SCI typically undergo rehabilitation for many years after the injury, and BCIs have begun to be tested for their benefits when included in SCI rehabilitation sessions. There are several ways for BCI systems to be used in SCI rehabilitation, which include virtual reality, exoskeletons, and neuroprosthesis. When using these methods as an output for a BCI system, SCI patients experience numerous benefits, most notably being an increase in mobility in the paralyzed region of their body. While there are several advantages to using BCIs for SCI rehabilitation, there are also several challenges that need to be addressed. In this chapter, we will discuss the current potential of BCIs for SCI rehabilitation, as well as what areas of this field need to be improved in the future.
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Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., … & Peckham, P. H. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The Lancet, 389(10081), 1821–1830.
Alam, M., Rodrigues, W., Pham, B. N., & Thakor, N. V. (2016). Brain-machine interface facilitated neurorehabilitation via spinal stimulation after spinal cord injury: Recent progress and future perspectives. Brain Research, 1646, 25–33.
Ang, K. K., Chua, K. S. G., Phua, K. S., Wang, C., Chin, Z. Y., Kuah, C. W. K., et al. (2015). A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clinical EEG and Neuroscience, 46(4), 310–320.
Blabe, C. H., Gilja, V., Chestek, C. A., Shenoy, K. V., Anderson, K. D., & Henderson, J. M. (2015). Assessment of brain-machine interfaces from the perspective of people with paralysis. Journal of Neural Engineering, 12(4), 043002.
Blankertz, B., Muller, K. R., Krusienski, D. J., Schalk, G., Wolpaw, J. R., Schlogl, A., … & Birbaumer, N. (2006). The BCI competition III: Validating alternative approaches to actual BCI problems. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 153–159.
Bockbrader, M. A., Francisco, G., Lee, R., Olson, J., Solinsky, R., & Boninger, M. L. (2018). Brain computer interfaces in rehabilitation medicine. PM&R, 10(9), S233–S243.
Burwell, S., Sample, M., & Racine, E. (2017). Ethical aspects of brain computer interfaces: A scoping review. BMC Medical Ethics, 18(1), 60.
Capogrosso, M., Milekovic, T., Borton, D., Wagner, F., Moraud, E. M., Mignardot, J., … Courtine, G. (2016). A brain-spine interface alleviating gait deficits after spinal cord injury in primates. Nature, 539(7628), 284–288.
Chaudhary, U., Birbaumer, N., & Ramos-Murguialday, A. (2016). Brain–computer interfaces for communication and rehabilitation. Nature Reviews Neurology, 12(9), 513.
Colachis, S. C., Bockbrader, M. A., Zhang, M., Friedenberg, D. A., Annetta, N. V., Schwemmer, M. A., … Sharma, G. (2018). Dexterous control of seven functional hand movements using cortically-controlled transcutaneous muscle stimulation in a person with tetraplegia. Frontiers in Neuroscience, 12, 208.
Donati, A. R. C., Shokur, S., Morya, E., Campos, D. S. F., Moioli, R. C., Gitti, C. M., … Nicolelis, M. A. L. (2016). Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Scientific Reports, 6, 30383.
Foldes, S. T., Weber, D. J., & Collinger, J. L. (2015). MEG-based neurofeedback for hand rehabilitation. Journal of Neuroengineering and Rehabilitation, 12, 85. https://doi.org/10.1186/s12984-015-0076-7.
Frolov, A. A., Mokienko, O., Lyukmanov, R., Biryukova, E., Kotov, S., Turbina, L., et al. (2017). Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: a randomized controlled multicenter trial. Frontiers in neuroscience, 11, 400.
Glezerman, M. (2016). Yes, there is a female and a male brain: Morphology versus functionality. Proceedings of the National Academy of Sciences of the United States of America, 113(14), E1971.
Gorgey, A. S. (2018). Robotic exoskeletons: The current pros and cons. World Journal of Orthopedics, 9(9), 112–119.
Höller, Y., Thomschewski, A., Uhl, A., Bathke, A. C., Nardone, R., Leis, S., … Höller, P.(2018). HD-EEG based classification of motor-imagery related activity in patients with spinal cord injury. Frontiers in Neurology, 9, 955.
Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., & Grosse-Wentrup, M. (2016). Transfer learning in brain-computer interfaces. IEEE Computational Intelligence Magazine, 11(1), 20–31.
Kilgore, K. L., Bryden, A., Keith, M. W., Hoyen, H. A., Hart, R. L., Nemunaitis, G. A., et al. (2018). Evolution of neuroprosthetic approaches to restoration of upper extremity function in spinal cord injury. Topics in Spinal Cord Injury Rehabilitation, 24(3), 252–264.
Lahr, J., Schwartz, C., Heimbach, B., Aertsen, A., Rickert, J., & Ball, T. (2015). Invasive brain-machine interfaces: A survey of paralyzed patients’ attitudes, knowledge and methods of information retrieval. Journal of Neural Engineering, 12(4), 043001.
Lazarou, I., Nikolopoulos, S., Petrantonakis, P. C., Kompatsiaris, I., & Tsolaki, M. (2018). EEG-based brain-computer interfaces for communication and rehabilitation of people with motor impairment: A novel approach of the 21st century. Frontiers in Human Neuroscience, 12, 14.
Lebedev, M. A., & Nicolelis, M. A. L. (2017). Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation. Physiological Reviews, 97(2), 767–837.
Likitlersuang, J., Koh, R., Gong, X., Jovanovic, L., Bolivar-Tellería, I., Myers, M., … Márquez-Chin, C. (2018). EEG-controlled functional electrical stimulation therapy with automated grasp selection: A proof-of-concept study. Topics in Spinal Cord Injury Rehabilitation, 24(3), 265–274.
Louie, D. R., Eng, J. J., & Lam, T. (2015). Gait speed using powered robotic exoskeletons after spinal cord injury: a systematic review and correlational study. Journal of neuroengineering and rehabilitation, 12(1), 82.
Madhura, I., Alex, S., Drew, P., Theodore, D. S., Mark, A. E., Kosha, R., …, Ragini, V. (2014). Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences of the United States of America, 111(2), 823-828. https://doi.org/10.1073/pnas.1316909110.
Mateo, S., Di Rienzo, F., Bergeron, V., Guillot, A., Collet, C., & Rode, G. (2015). Motor imagery reinforces brain compensation of reach-to-grasp movement after cervical spinal cord injury. Frontiers in Behavioral Neuroscience, 9, 234.
McCrimmon, C. M., Ming Wang, n., Silva Lopes, L., Wang, P. T., Karimi-Bidhendi, A., Liu, C. Y., … Do, A. H. (2016). A small, portable, battery-powered brain-computer interface system for motor rehabilitation. Conference Proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2016, 2776–2779.
Pfurtscheller, G., Brunner, C., Schlögl, A., & Da Silva, F. L. (2006). Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage, 31(1), 153–159.
Pichiorri, F., Morone, G., Petti, M., Toppi, J., Pisotta, I., Molinari, M., et al. (2015). Brain–computer interface boosts motor imagery practice during stroke recovery. Annals of Neurology, 77(5), 851–865.
Pichiorri, F., Mrachacz-Kersting, N., Molinari, M., Kleih, S., Kübler, A., & Mattia, D. (2017). Brain-computer interface based motor and cognitive rehabilitation after stroke–state of the art, opportunity, and barriers: summary of the BCI Meeting 2016 in Asilomar. Brain-Computer Interfaces, 4(1–2), 53–59.
Salisbury, D. B., Driver, S., & Parsons, T. D. (2015). Brain-computer interface targeting non-motor functions after spinal cord injury: A case report. Spinal Cord, 53(S1), S25.
Salisbury, D. B., Dahdah, M., Driver, S., Parsons, T. D., & Richter, K. M. (2016). Virtual reality and brain computer interface in neurorehabilitation. Proceedings (Baylor University. Medical Center), 29(2), 124–127.
Salisbury, D. B., Parsons, T. D., Monden, K. R., Trost, Z., & Driver, S. J. (2016b). Brain-computer interface for individuals after spinal cord injury. Rehabilitation Psychology, 61(4), 435–441.
Scandola, M., Aglioti, S. M., Pozeg, P., Avesani, R., & Moro, V. (2017). Motor imagery in spinal cord injured people is modulated by somatotopic coding, perspective taking, and post-lesional chronic pain. Journal of Neuropsychology, 11(3), 305–326.
Slutzky, M. W. (2018). Brain-machine interfaces: powerful tools for clinical treatment and neuroscientific investigations. The Neuroscientist, 1073858418775355.
Tariq, M., Trivailo, P. M., & Simic, M. (2018). EEG-based BCI control schemes for lower-limb assistive-robots. Frontiers in Human Neuroscience, 12, 312.
Thomschewski, A., Ströhlein, A., Langthaler, P. B., Schmid, E., Potthoff, J., Höller, P., … Höller, Y. (2017). Imagine there is no plegia. mental motor imagery difficulties in patients with traumatic spinal cord injury. Frontiers in Neuroscience, 11, 689.
Vidal, J. J. (1973). Toward direct brain-computer communication. Annual review of Biophysics and Bioengineering, 2(1), 157–180.
Wirz, M., van Hedel, & Hubertus, J. A. (2018). Balance, gait, and falls in spinal cord injury. Handbook of Clinical Neurology, 159, 367–384.
Yoshida, N., Hashimoto, Y., Shikota, M., & Ota, T. (2016). Relief of neuropathic pain after spinal cord injury by brain-computer interface training. Spinal Cord Series and Cases, 2, 16021.
Zanini, P., Congedo, M., Jutten, C., Said, S., & Berthoumieu, Y. (2018). Transfer learning: a Riemannian geometry framework with applications to brain–computer interfaces. IEEE Transactions on Biomedical Engineering, 65(5), 1107–1116.
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Merante, A., Zhang, Y., Kumar, S., Nam, C.S. (2020). Brain–Computer Interfaces for Spinal Cord Injury Rehabilitation. In: Nam, C. (eds) Neuroergonomics. Cognitive Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-34784-0_16
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