Skip to main content

Brain-Computer Interfaces and Therapy

  • Chapter
  • First Online:
  • 1322 Accesses

Part of the book series: The International Library of Ethics, Law and Technology ((ELTE,volume 12))

Abstract

In recent times the idea that brain–computer interface (BCI) technology can be used to control brain mechanisms to sustain recovery and improve functions has been advanced and tested by different groups. This new development in BCI research and application raises ethical issues quite different from those previously addressed. After describing recent BCI-driven applications in neurological rehabilitation we focus on two main ethical issues stemming from present BCI therapeutic applications, namely the potential occurrence of iatrogenic effects because of potentiating maladaptive circuits and difficulties in addressing cognitive/behavioral performances in an uncontrolled loop.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   139.99
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

Learn about institutional subscriptions

References

  • Allison, B. 2011. Trends in BCI research. XRDS the ACM Magazine for Students 18: 18–22.

    Article  Google Scholar 

  • Ang, K.K., C. Guan, K.S.G. Chua, et al. 2010. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. In Conference proceedings: Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 2010, Buenos Aires, Argentina 5549–5552.

    Google Scholar 

  • Broetz, D., C. Braun, C. Weber, S.R. Soekadar, A. Caria, and N. Birbaumer. 2010. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: A case report. Neurorehabilitation and Neural Repair 24: 674–679.

    Article  Google Scholar 

  • Buch, E., C. Weber, L.G. Cohen, et al. 2008. Think to move: A neuromagnetic Brain-Computer Interface (BCI) system for chronic stroke. Stroke 39: 910–917.

    Article  Google Scholar 

  • Bundy, D.T., M. Wronkiewicz, M. Sharma, D.W. Moran, M. Corbetta, and E.C. Leuthardt. 2012. Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors. Journal of Neural Engineering 9: 036011. doi:10.1088/1741-2560/9/3/036011.

  • Caria, A., C. Weber, D. Broetz, et al. 2011. Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology 48: 578–582.

    Article  Google Scholar 

  • Clausen, J. 2011. Conceptual and ethical issues with brain-hardware interfaces. (Miscellaneous Article). Current Opinion in Psychiatry 24: 495–501.

    Google Scholar 

  • Daly, J.J., and J.R. Wolpaw. 2008. Brain-computer interfaces in neurological rehabilitation. Lancet Neurology 7: 1032–1043.

    Article  Google Scholar 

  • Daly, J.J., R. Cheng, J. Rogers, K. Litinas, K. Hrovat, and M. Dohring. 2009. Feasibility of a new application of noninvasive Brain Computer Interface (BCI): A case study of training for recovery of volitional motor control after stroke. Journal of Neurologic Physical Therapy 33: 203–211.

    Article  Google Scholar 

  • de Zambotti, M., M. Bianchin, L. Magazzini, G. Gnesato, and A. Angrilli. 2012. The efficacy of EEG neurofeedback aimed at enhancing sensory-motor rhythm theta ratio in healthy subjects. Experimental Brain Research 221: 69–74.

    Article  Google Scholar 

  • Dimyan, M.A., and L.G. Cohen. 2011. Neuroplasticity in the context of motor rehabilitation after stroke. Nature Reviews Neurology 7: 76–85.

    Article  Google Scholar 

  • Egner, T., and J.H. Gruzelier. 2001. Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans. Neuroreport 12: 4155–4159.

    Article  Google Scholar 

  • Egner, T., and J.H. Gruzelier. 2003. Investigating the use of EEG biofeedback in the enhancement of music performance. Journal of Psychophysiology 17: 104.

    Google Scholar 

  • Egner, T., and J.H. Gruzelier. 2004. EEG Biofeedback of low beta band components: Frequency-specific effects on variables of attention and event-related brain potentials. Clinical Neurophysiology 115: 131–139.

    Article  Google Scholar 

  • Farah, M.J. 2002. Emerging ethical issues in neuroscience. Nature Neuroscience 5: 1123–1129.

    Article  Google Scholar 

  • Gomez-Rodrig, M. 2011. Closing the sensorimotor loop: Haptic feedback facilitates decoding of motor imagery. Journal of Neural Engineering 8: 036005.

    Article  Google Scholar 

  • Grosse-Wentrup, M., D. Mattia, and K. Oweiss. 2011. Using brain-computer interfaces to induce neural plasticity and restore function. Journal of Neural Engineering 8: 025004. doi: 10.1088/1741-2560/8/2/025004.

    Article  Google Scholar 

  • Gustin, S.M., P.J. Wrigley, S.C. Gandevia, J.W. Middleton, L.A. Henderson, and P.J. Siddall. 2008. Movement imagery increases pain in people with neuropathic pain following complete thoracic spinal cord injury. Pain 137: 237–244.

    Article  Google Scholar 

  • Guttman, A., A. Burstin, R. Brown, S. Bril, and R. Dickstein. 2012. Motor imagery practice for improving sit to stand and reaching to grasp in individuals with poststroke hemiparesis. Topics in Stroke Rehabilitation 19: 306–319.

    Article  Google Scholar 

  • Hardy, K., K. Sprague, A. Suever, P. Levine, and S.J. Page. 2010. Combined bracing, electrical stimulation, and functional practice for chronic, upper-extremity spasticity. American Journal of Occupational Therapy 64: 720–726.

    Article  Google Scholar 

  • Heinrichs, J.H. 2012. The promises and perils of non-invasive brain stimulation. International Journal of Law and Psychiatry 35: 121–129.

    Article  Google Scholar 

  • Ietswaart, M., M. Johnston, H.C. Dijkerman, et al. 2011. Mental practice with motor imagery in stroke recovery: Randomized controlled trial of efficacy. Brain 134: 1373–1386.

    Article  Google Scholar 

  • Jeannerod, M., and J. Decety. 1995. Mental motor imagery: A window into the representational stages of action. Current Opinion in Neurobiology 5: 727–732.

    Article  Google Scholar 

  • Kadosh, R.C., N. Levy, J. O'Shea, N. Shea, and J. Savulescu. 2012. The neuroethics of non-invasive brain stimulation. Current Biology 22: R108–R111.

    Article  Google Scholar 

  • Kotchoubey, B., S. Busch, U. Strehl, and N. Birbaumer. 2001. Changes in EEG power spectra during SCP neurofeedback training in epilepsy. Journal of Psychophysiology 15: 134–135.

    Google Scholar 

  • Liu, K.P., C.C. Chan, T.M. Lee, and C.W. Hui-Chan. 2004. Mental imagery for promoting relearning for people after stroke: A randomized controlled trial. Archives of Physical Medicine and Rehabilitation 85: 1403–1408.

    Article  Google Scholar 

  • Mattia, D., F. Pichiorri, M. Molinari, and R. Rupp. 2013. Brain computer interface for hand motor function restoration and rehabilitation. In Towards practical brain-computer interfaces, ed. B.Z. Allison, S. Dunne, R. Leeb, R.M. Del, and A. Nijholt, 131–153. Berlin/Heidelberg: Springer.

    Google Scholar 

  • Mulder, T. 2007. Motor imagery and action observation: Cognitive tools for rehabilitation. Journal of Neural Transmission 114: 1265–1278.

    Article  Google Scholar 

  • Nagaoka, T., K. Sakatani, T. Awano, et al. 2010. Development of a new rehabilitation system based on a brain-computer interface using near-infrared spectroscopy. In Oxygen transport to tissue XXXI, ed. E. Takahashi and D.F. Bruley, 497–503. New York: Springer.

    Chapter  Google Scholar 

  • Neuper, C., G.R. Müller-Putz, R. Scherer, and G. Pfurtscheller. 2006. Motor imagery and EEG-based control of spelling devices and neuroprostheses. Event-Related Dynamics of Brain Oscillations 159: 393–409.

    Article  Google Scholar 

  • Nudo, R.J. 2007. Postinfarct cortical plasticity and behavioral recovery. Stroke 38: 840–845.

    Article  Google Scholar 

  • Pichiorri, F., F.D. Fallani, F. Cincotti, et al. 2011. Sensorimotor rhythm-based brain-computer interface training: The impact on motor cortical responsiveness. Journal of Neural Engineering 8: 025020. doi: 10.1088/1741-2560/8/2/025020.

  • Pichiorri, F., G. Morone, F. Cincotti, et al. 2012. Clinical trial design to validate a BCI-supported task-specific training in neurorehabilitation after stroke. European Journal of Neurology 19: 566.

    Google Scholar 

  • Prasad, G., P. Herman, D. Coyle, S. McDonough, and J. Crosbie. 2010. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: A feasibility study. Journal of NeuroEngineering and Rehabilitation 7: 60. doi:10.1186/1743-0003-7-60.

  • Rachul, C., and A. Zarzeczny. 2012. The rise of neuroskepticism. International Journal of Law and Psychiatry 35: 77–81.

    Article  Google Scholar 

  • Raymond, J., I. Sajid, L.A. Parkinson, and J.H. Gruzelier. 2005a. Biofeedback and dance performance: A preliminary investigation. Applied Psychophysiology and Biofeedback 30: 65–73.

    Article  Google Scholar 

  • Raymond, J., C. Varney, L.A. Parkinson, and J.H. Gruzelier. 2005b. The effects of alpha/theta neurofeedback on personality and mood. Cognitive Brain Research 23: 287–292.

    Article  Google Scholar 

  • Rockstroh, B., T. Elbert, N. Birbaumer, et al. 1993. Cortical self-regulation in patients with epilepsies. Epilepsy Research 14: 63–72.

    Article  Google Scholar 

  • Ros, T., M.A. Munneke, D. Ruge, J.H. Gruzelier, and J.C. Rothwell. 2010. Endogenous control of waking brain rhythms induces neuroplasticity in humans. European Journal of Neuroscience 31: 770–778.

    Article  Google Scholar 

  • Rossini, P.M., M.A.N. Ferilli, and F. Ferreri. 2012. Cortical plasticity and brain computer interface. European Journal of Physical and Rehabilitation Medicine 48: 307–312.

    Google Scholar 

  • Schneider, M.-J., J.J. Fins, and J.R. Wolpaw. 2013. Ethical issues in BCI research. In Brain-computer interfaces principles and practice, ed. Jonathan Wolpaw and Elizabeth Winter Wolpaw, 373–383. Oxford University Press, Oxford.

    Google Scholar 

  • Serruya, M.D., and M.J. Kahana. 2008. Techniques and devices to restore cognition. Behavioural Brain Research 192: 149–165.

    Article  Google Scholar 

  • Sharma, N., and L.G. Cohen. 2012. Recovery of motor function after stroke. Developmental Psychobiology 54: 254–262.

    Article  Google Scholar 

  • Shih, J.J., D.J. Krusienski, and J.R. Wolpaw. 2012. Brain-computer interfaces in medicine. Mayo Clinic Proceedings 87: 268–279.

    Article  Google Scholar 

  • Short, S.E., A. Tenute, and D.L. Feltz. 2005. Imagery use in sport: Mediational effects for efficacy. Journal of Sports Sciences 23: 951–960.

    Article  Google Scholar 

  • Strehl, U., B. Kotchoubey, T. Trevorrow, and N. Birbaumer. 2005. Predictors of seizure reduction after self-regulation of slow cortical potentials as a treatment of drug-resistant epilepsy. Epilepsy & Behavior 6: 156–166.

    Article  Google Scholar 

  • Strehl, U., U. Leins, G. Goth, C. Klinger, T. Hinterberger, and N. Birbaumer. 2006. Self-regulation of slow cortical potentials: A new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics 118: E1530–E1540.

    Article  Google Scholar 

  • Strehl, U., C. Gani, S. Kaller, and N. Birbaumer. 2007. Long term stability of neurofeedback in children with ADHD. Journal of Neural Transmission 114: LIX–LLX.

    Google Scholar 

  • Tamburrini, G. 2009. Brain to computer communication: Ethical perspectives on interaction models. Neuroethics 2: 137–149.

    Article  Google Scholar 

  • Tennison, M.N., and J.D. Moreno. 2012. Neuroscience, ethics, and national security: The state of the art. PLoS Biology 10: e1001289. doi:10.1371/journal.pbio.1001289.

  • Thornton, K.E., and D.P. Carmody. 2009. Traumatic brain injury rehabilitation: QEEG biofeedback treatment protocols. Applied Psychophysiology and Biofeedback 34: 59–68.

    Article  Google Scholar 

  • Vernon, D., N. Cooper, T. Egner, et al. 2003. The influence of SMR and theta neurofeedback training on working memory performance. Journal of Psychophysiology 17: 109.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marco Molinari .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Mattia, D., Molinari, M. (2014). Brain-Computer Interfaces and Therapy. In: Grübler, G., Hildt, E. (eds) Brain-Computer-Interfaces in their ethical, social and cultural contexts. The International Library of Ethics, Law and Technology, vol 12. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8996-7_4

Download citation

Publish with us

Policies and ethics