Skip to main content

BMIs for Motor Rehabilitation: Key Concepts and Challenges

  • Chapter
  • First Online:
Emerging Therapies in Neurorehabilitation

Part of the book series: Biosystems & Biorobotics ((BIOSYSROB,volume 4))

Abstract

Controlling devices using the mind has always fascinated humans. The number of opportunities that have now been opened is unimaginable—for example, the possibility of just thinking while a robot does the task for you or commanding an exoskeleton attached to your body that augments your strength and agility. But not just that: try to imagine the possibility of feeling it as a part of your body or to receive sensory feedback from artificial sensors placed away from your own body. Possibilities like these, very common in science fiction movies in the last decades, are now becoming a reality. Our brain is very powerful, and scientists have devoted much effort to understand and use this power. In recent years, new technologies helped scientists to create brain-machine interfaces (BMIs), bringing the possibility to record and analyze brain signals. By means of thousands of tiny electrodes implanted inside the brain, it is now possible to record this electrical activity, and from these signals, the intentions of the user can be decoded and exploited to command robotic devices. Based on this new technology, a user would be able to control a robotic device while feeling real sensations of what the device is touching, grasping or holding. The most important field where this emerging technology is being applied is motor rehabilitation. Stroke, Parkinson and spinal cord injury patients may have their quality of life really improved by this technology in the very near future.

All authors made equal contributions to the study and are listed in alphabetical order.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Carmena JM et al (2003) Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol 1(2):e42

    Article  Google Scholar 

  • Carmena JM, Lebedev MA, Henriquez CS, Nicolelis MA (2005) Stable ensemble performance with single-neuron variability during reaching movements in primates. J Neurosci 25(46):10712–10716

    Article  Google Scholar 

  • Belda-Lois JM, Mena-del Horno S, Bermejo-Bosch I, Moreno JC, Pons JL, Farina D, Iosa M, Molinari M, Tamburella F, Ramos A, Caria A, Solis-Escalante T, Brunner C, Rea M (2011) (December) Rehabilitation of gait after stroke: a review towards a top-down approach. J Neuroeng Rehabil 13(8):66

    Google Scholar 

  • Daly J, Cheng R, Rogers J, Litinas K, Hrovat K, Dohring M (2009) (December) Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther 33(4):203–211

    Google Scholar 

  • Díaz I, Gil JJ, Sánchez E (2011) Lower-limb robotic rehabilitation: literature review and challenges. J Robotics 2011:11 (Article no. 759764)

    Google Scholar 

  • Dobkin BH (2007) Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol 579(Pt 3):637–642

    Article  Google Scholar 

  • Donoghue JP et al (2007) Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J Physiol 579:603–611

    Article  Google Scholar 

  • Fuentes R et al. (2009) (March 20) Spinal cord stimulation restores locomotion in animal models of Parkinson’s disease. Science 323(5921):1578–1582

    Google Scholar 

  • Haufe S, Treder MS, Gugler MF, Sagebaum M, Curio G, Blankertz B (2011) EEG potentials predict upcoming emergency brakings during simulated driving. J. NeuralEng. 8:056001

    Article  Google Scholar 

  • Head H, Holmes G (1911) Sensory disturbances from cerebral lesion. Brain 34:102–254

    Article  Google Scholar 

  • Hidler J, Nichols D, Pelliccio M, Brady K (2005) Advances in the understanding and treatment of stroke impairment using robotic devices. Top Stroke Rehabil 12:22–35

    Article  Google Scholar 

  • Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442:164–171

    Article  Google Scholar 

  • Kennedy PR, Bakay RAE, Moore MM, Adams K, Goldwaithe J (2000) Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng 8:198

    Article  Google Scholar 

  • Kim SP et al (2008) Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J Neural Eng 5:455

    Article  Google Scholar 

  • Lebedev MA, Nicolelis MAL (2006) Brain-machine interfaces: past, present and future, Review. Trends Neurosci 29(9) 536–546

    Google Scholar 

  • Mangold T, Keller S, Curt A, Dietz V (2005) Transcutaneous functional electrical stimulation for grasping in subjects with cervical spinal cord injury. Spinal Cord 43:1–13

    Article  Google Scholar 

  • Mak JN, Wolpaw JR (2009) Clinical applications of brain–computer interfaces: current state and future prospects. IEEE Rev Biomed Eng 2:187–199

    Google Scholar 

  • Millán JdR et al. (2010) Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Frontiers Neurosci 4:161

    Google Scholar 

  • Lebedev MA, Nicolelis MAL (2011) Toward a whole-body neuroprosthetic, Elsevier. Prog Brain Res 194:47–60

    Google Scholar 

  • Nicolelis MAL (2001) Actions from thoughts. Nature 409:403–407

    Article  Google Scholar 

  • Nicolelis MAL (ed) (2011) Beyond boundaries: the new neuroscience of connecting brains with machines—and how it will change our lives. Times book, New York, pp 1–18

    Google Scholar 

  • Nicolelis MAL (2012) (September) Mind to motion. Sci Am 307:58–63

    Google Scholar 

  • O’Doherty JE, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, Nicolelis MAL (2011) Active tactile exploration using a brain-machine-brain interface. Nature. doi:10.1038

    Google Scholar 

  • Polikovet VS, Tresco PA, Reichert WM (2005) Response of brain tissue to chronically implanted neural electrodes. J Neurosci Methods 148(1):1–18

    Article  Google Scholar 

  • Pons JL (2008) Wearable Robots: Biomechatronic Exoskeletons. John Wiley & Sons, Ltd

    Book  Google Scholar 

  • Porbadnigk AK, Antons J-N, Blankertz B, Treder MS, Schleicher R, Möller S, Curio G (2010) Using ERPs for assessing the (sub)conscious perception of noise. Conference proceedings-IEEE engineering in medicine and biology society, pp 2690–2693

    Google Scholar 

  • Porbadnigk AK, Scholler S, Blankertz B, Ritz A, Born M, Scholl R, Müller K-R, Curio G, Treder MS (2011) Revealing the neural response to imperceptible peripheral flicker with machine learning. Conference proceedings-IEEE engineering in medicine and biology society, pp 3692–3695

    Google Scholar 

  • Prasad G, Herman P, Coyle D, McDonough S, Crosbie J (2009) Using motor imagery based brain-computer interface for post-stroke rehabilitation. Neural Engineering, 2009. NER ’09. 4th International IEEE/EMBS Conference

    Google Scholar 

  • Schwartz AB, Cui XT, Weber DJ, Moran DW (2006) Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 52(1):205–220

    Google Scholar 

  • Schmidt EM (1980) Single neuron recording from motor cortex as possible source of signals for control of external devices. Ann Biomed Eng 8:339–349

    Google Scholar 

  • Serruya MD et al (2002) Instant neural control of a movement signal. Nature 416:141–142

    Article  Google Scholar 

  • Shoham S, Halgren E, Maynard EM, Normann RA (2001) Motor-cortical activity in tetraplegics. Nature 413:793

    Article  Google Scholar 

  • Taylor DM, Tillery SI, Schwartz AB (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296:1829–1832

    Article  Google Scholar 

  • Thorsen R, Spadone R, Ferrarin M (2001) A pilot study of myoelectrically controlled FES of upper extremity. IEEE Trans Neural Syst Rehabil Eng 9:161–168

    Article  Google Scholar 

  • Ushiba J (2010) (February) Brain-machine interface-current status and future prospects. Brain Nerve 62(2):101–111

    Google Scholar 

  • Wang W, Collinger JL, Perez MA, Tyler-Kabara EC, Cohen LG, Birbaumer N, Brose SW, Schwartz AB, Boninger ML, Weber DJ (2010) Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. Phys Med Rehabil Clin N Am 21(1):157–178

    Article  MATH  Google Scholar 

  • Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) (June) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehab Eng 8:164–173

    Google Scholar 

  • Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791

    Article  Google Scholar 

  • Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs SJ, Srinivasan MA, Nicolelis MA (2000) Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408(6810):361–365

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Magdo Bortole .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bortole, M., Controzzi, M., Pisotta, I., Úbeda, A. (2014). BMIs for Motor Rehabilitation: Key Concepts and Challenges. In: Pons, J., Torricelli, D. (eds) Emerging Therapies in Neurorehabilitation. Biosystems & Biorobotics, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38556-8_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38556-8_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38555-1

  • Online ISBN: 978-3-642-38556-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics