Brain-Computer Interfaces and Therapy

  • Donatella Mattia
  • Marco MolinariEmail author
Part of the The International Library of Ethics, Law and Technology book series (ELTE, volume 12)


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.


  1. Allison, B. 2011. Trends in BCI research. XRDS the ACM Magazine for Students 18: 18–22.CrossRefGoogle Scholar
  2. 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
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.
  6. 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.CrossRefGoogle Scholar
  7. Clausen, J. 2011. Conceptual and ethical issues with brain-hardware interfaces. (Miscellaneous Article). Current Opinion in Psychiatry 24: 495–501.Google Scholar
  8. Daly, J.J., and J.R. Wolpaw. 2008. Brain-computer interfaces in neurological rehabilitation. Lancet Neurology 7: 1032–1043.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. Dimyan, M.A., and L.G. Cohen. 2011. Neuroplasticity in the context of motor rehabilitation after stroke. Nature Reviews Neurology 7: 76–85.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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
  14. 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.CrossRefGoogle Scholar
  15. Farah, M.J. 2002. Emerging ethical issues in neuroscience. Nature Neuroscience 5: 1123–1129.CrossRefGoogle Scholar
  16. Gomez-Rodrig, M. 2011. Closing the sensorimotor loop: Haptic feedback facilitates decoding of motor imagery. Journal of Neural Engineering 8: 036005.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. Heinrichs, J.H. 2012. The promises and perils of non-invasive brain stimulation. International Journal of Law and Psychiatry 35: 121–129.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Jeannerod, M., and J. Decety. 1995. Mental motor imagery: A window into the representational stages of action. Current Opinion in Neurobiology 5: 727–732.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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
  26. 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.CrossRefGoogle Scholar
  27. 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
  28. Mulder, T. 2007. Motor imagery and action observation: Cognitive tools for rehabilitation. Journal of Neural Transmission 114: 1265–1278.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. Nudo, R.J. 2007. Postinfarct cortical plasticity and behavioral recovery. Stroke 38: 840–845.CrossRefGoogle Scholar
  32. 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.
  33. 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
  34. 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.
  35. Rachul, C., and A. Zarzeczny. 2012. The rise of neuroskepticism. International Journal of Law and Psychiatry 35: 77–81.CrossRefGoogle Scholar
  36. 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.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. Rockstroh, B., T. Elbert, N. Birbaumer, et al. 1993. Cortical self-regulation in patients with epilepsies. Epilepsy Research 14: 63–72.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. 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
  41. 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
  42. Serruya, M.D., and M.J. Kahana. 2008. Techniques and devices to restore cognition. Behavioural Brain Research 192: 149–165.CrossRefGoogle Scholar
  43. Sharma, N., and L.G. Cohen. 2012. Recovery of motor function after stroke. Developmental Psychobiology 54: 254–262.CrossRefGoogle Scholar
  44. Shih, J.J., D.J. Krusienski, and J.R. Wolpaw. 2012. Brain-computer interfaces in medicine. Mayo Clinic Proceedings 87: 268–279.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 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.CrossRefGoogle Scholar
  47. 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.CrossRefGoogle Scholar
  48. 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
  49. Tamburrini, G. 2009. Brain to computer communication: Ethical perspectives on interaction models. Neuroethics 2: 137–149.CrossRefGoogle Scholar
  50. 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.
  51. Thornton, K.E., and D.P. Carmody. 2009. Traumatic brain injury rehabilitation: QEEG biofeedback treatment protocols. Applied Psychophysiology and Biofeedback 34: 59–68.CrossRefGoogle Scholar
  52. 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

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.IRCCS Santa Lucia FoundationRomeItaly

Personalised recommendations