Brain-Machine Interfaces for Assistive Robotics

  • Enrique HortalEmail author
  • Andrés Úbeda
  • Eduardo Iáñez
  • José M. Azorín
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 106)


Motor disability may be caused by many different conditions. The most common one is a cerebrovascular accident (CVA) which occurs when the blood supply to the brain stops [1]. If the length of this interruption is longer than several seconds, brain cells can die causing a permanent damage in the patient. When this damage occurs in the brain areas responsible for motor control, the patients may suffer permanent or temporal loss of mobility, coordination and control of their limbs. Another important cause of motor disability is due to spinal cord injury (SCI), which provokes the total loss of sensibility and movement capability below the level of the injury [2]. In this case, the patient assistance must be purely based on motor substitution, given that it is impossible to perform a rehabilitation procedure. Finally, less frequent illnesses and diseases may cause motor disfunctions, such as cerebral palsy, spina bifida, muscular dystrophy, amyotrophic lateral sclerosis (ALS) or central nervous system diseases such as Parkinson syndrome or Huntington disease.


Amyotrophic Lateral Sclerosis Motor Imagery Amyotrophic Lateral Sclerosis Patient Mental Task Brain Computer Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chung, C.S., Caplan, L.R.: Stroke and other Neurovascular Disorders. In: Textbook of Clinical Neurology, 3rd edn., ch. 45. Elsevier (2007)Google Scholar
  2. 2.
    Ling, G.S.F.: Traumatic Brain Injury and Spinal Cord Injury. In: Goldmans Cecil Medicine, 24th edn., ch. 406. Elsevier (2011)Google Scholar
  3. 3.
    Nicolelis, M.A.L.: Actions from thoughts. Nature 409, 403–407 (2001)CrossRefGoogle Scholar
  4. 4.
    Carmena, J.M., et al.: Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology 1(2), E42 (2003)CrossRefGoogle Scholar
  5. 5.
    Hochberg, L.R., et al.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006)CrossRefGoogle Scholar
  6. 6.
    Velliste, M., Perel, S., Spalding, M.C., Whitford, A.S., Schwartz, A.B.T.: Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008)CrossRefGoogle Scholar
  7. 7.
    del R. Millan, J., et al.: Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Frontiers in Neuroscience 4, 161 (2010)Google Scholar
  8. 8.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002)CrossRefGoogle Scholar
  9. 9.
    Daly, J.J., Wolpaw, J.R.: Brain-computer interfaces in neurological rehabilitation. Lancet Neurology 7, 1032–1043 (2008)CrossRefGoogle Scholar
  10. 10.
    Birbaumer, N., Cohen, L.G.: Brain-computer interfaces: Communication and restoration of movement in paralysis. J. Physiology 579, 621–636 (2007)CrossRefGoogle Scholar
  11. 11.
    Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R.: Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation. Biomedizinische Technik 51, 57–63 (2006)CrossRefGoogle Scholar
  12. 12.
    Pfurtscheller, G., Müller-Putz, G.R., Scherer, R., Neuper, C.: Rehabilitation with brain-computer interface systems. Computer 41(10), 58–65 (2008)CrossRefGoogle Scholar
  13. 13.
    Mak, J.N., Wolpaw, J.R.: Clinical applications of brain-computer interfaces: Current state and future prospects. IEEE Rev. Biomed. Eng. 2, 187–199 (2009)CrossRefGoogle Scholar
  14. 14.
    Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Perelmouter, J., Taub, E., Flor, H.: A spelling device for the paralysed. Nature 398, 297–298 (1999)CrossRefGoogle Scholar
  15. 15.
    Obermaier, B., Müller, G.R., Pfurtscheller, G.: Virtual keyboard controlled by spontaneous EEG activity. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 422–426 (2003)CrossRefGoogle Scholar
  16. 16.
    Müller, K.-R., Blankertz, B.: Toward noninvasive brain-computer interfaces. IEEE Signal Process. Mag. 23, 125–128 (2006)CrossRefGoogle Scholar
  17. 17.
    Mugler, E., Bensch, M., Halder, S., Rosenstiel, W., Bogdan, M., Birbaumer, N., Kübler, A.: Control of an internet browser using P300 event-related potential. Int. J. Bioelectromagn. 10, 56–63 (2008)Google Scholar
  18. 18.
    Sirvent, J.L., Iáñez, E., Úbeda, A., Azorín, J.M.: Visual evoked potential-based brain–machine interface applications to assist disabled people. Expert Systems with Applications 39(9), 7908–7918 (2012)CrossRefGoogle Scholar
  19. 19.
    Sellers, E.W., Donchin, E.: A P300-based brain-computer interface: Initial tests by ALS patients. Clinical Neurophysiology 117(3), 538–548 (2006)CrossRefGoogle Scholar
  20. 20.
    Iturrate, I., Antelis, J.M., Kubler, A., Minguez, J.: A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Transactions on Robotics 25(3), 614–627 (2009)CrossRefGoogle Scholar
  21. 21.
    Carlson, T., del R. Millán, J.: Brain-controlled wheelchairs: a robotic architecture. IEEE Robotics and Automation Magazine 20(1), 65–73 (2013)Google Scholar
  22. 22.
    Farwell, L.A., Donchin, E.: Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroenceph. Clin. Neurophysiol. 70, 510–523 (1998)CrossRefGoogle Scholar
  23. 23.
    Allison, B.Z., Pineda, J.A.: ERPs evoked by different matrix sizes: Implications for a brain computer interface (BCI) system. IEEE Trans. Neural Sys. Rehab. Eng. 11, 110–113 (2003)CrossRefGoogle Scholar
  24. 24.
    Bensch, M., Karim, A.A., Mellinger, J., Hinterberger, T., Tangermann, M., Rosenstiel, W., Birbaumer, N.: Nessi: An EEG-controlled web browser for severely paralyzed patients. Computational Intelligence and Neuroscience (2007)Google Scholar
  25. 25.
    Eimer, M.: The N2pc component as an indicator of attentional selectivity. Electroencephalography and clinical Neurophysiology 99, 225–234 (1996)CrossRefGoogle Scholar
  26. 26.
    Kiss, M., Van Velzen, J., Eimer, M.: The N2pc component and its links to attention shift and spatially selective visual processing. Psychophysiology 45(2), 240–249 (2008)CrossRefGoogle Scholar
  27. 27.
    Purves, D., Augustine, G., Fitzpatrick, D., Hall, W., LaMantia, A.S., McNamara, J., Williams, S.: Neurociencia, 3rd edn. Editorial Medica Panamericana (2006)Google Scholar
  28. 28.
    Touyama, H., Hirose, M.: Non-target photo images in oddball paradigm improve EEG-based personal identification rates. In: Engineering in Medicine and Biology Society, pp. 4118–4121 (2008)Google Scholar
  29. 29.
    Luck, S.J., Heinze, H.J., Mangun, G.R., Hillyard, S.A.: Visual event-related potentials index focused attention within bilateral stimulus arrays. Functional Dissociation of P1 and N1 components. Electroencephalography and clinical Neurophysiology 75, 528–542 (1990)CrossRefGoogle Scholar
  30. 30.
    Johnson, G.D., Krusienski, D.J.: Ensemble SWLDA classifiers for the P300 speller. In: Jacko, J.A. (ed.) HCI International 2009, Part II. LNCS, vol. 5611, pp. 551–557. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  31. 31.
    American Electroencephalographic Society: American Electroencephalography Society guidelines for standard electrode position nomenclature. Journal of Clinical Neurophysiology 8(2), 200–202 (1991)Google Scholar
  32. 32.
    Krusienski, D.J., Sellers, E.W., Cabestaing, F., Bayoudh, S., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: A comparasion of classification tecniques for the P300 Speller. Journal of Neural Engineering 3, 299–305 (2006)CrossRefGoogle Scholar
  33. 33.
    Mirghasemi, H., Fazel-Rezai, R.: Analysis of P300 classifiers in brain computer interface speller. Engineering in Medicine and Biology Society 1, 6205–6208 (2006)Google Scholar
  34. 34.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering 51(6), 1034–1040 (2004)CrossRefGoogle Scholar
  35. 35.
    Iáñez, E., Azorín, J.M., Úbeda, A., Ferrández, J.M., Fernández, E.: Mental tasks-based brain–robot interface. Robotics and Autonomous Systems 58(12), 1238–1245 (2010)CrossRefGoogle Scholar
  36. 36.
    Inoue, S., Akiyama, Y., Izumi, Y., Nishijima, S.: The development of BCI using alpha waves for controlling the robot arm. IEICE Transactions on Communications 91(7), 2125–2132 (2008)CrossRefGoogle Scholar
  37. 37.
    Decety, J., Lindgren, M.: Sensation of effort and duration of mentally executed actions. Scandinavian Journal of Psychology 32, 97–104 (2001)CrossRefGoogle Scholar
  38. 38.
    Lotte, F., Congedo, M., Lcuyer, A., Lamarche, F., Arnald, B.: A review of classification algorithms for EEG-based Brain-Computer Interfaces. Journal of Neural Engineering 4(2), 1–13 (2013)CrossRefGoogle Scholar
  39. 39.
    Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering 4(2), 35–57 (2001)Google Scholar
  40. 40.
    Wang, L., Xu, G., Wang, J., Yang, S., Wang, J.: Motor imagery BCI research based on sample entropy and SVM. In: International Conference on Electromagnetic Field Problems and Applications, pp. 313–316 (2001)Google Scholar
  41. 41.
    Arbabi, E., Shamsollahi, M.B., Sameni, R.: Comparison between effective features used for the Bayesian and the SVM classifiers in BCI. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 5365–5368 (2001)Google Scholar
  42. 42.
    Vapnik, V.: Statistical Learning Theory. Ed. Wily, New York (1998)zbMATHGoogle Scholar
  43. 43.
    Hortal, E., Úbeda, A., Iáñez, E., Planelles, D., Azorín, J.M.: Online classification of two mental tasks using a SVM-based BCI system. In: Neural Engineering Conference 2013, pp. 1307–1310 (2013)Google Scholar
  44. 44.
    Úbeda, A., Iáñez, E., Badesa, F.J., Morales, R., Azorín, J.M., García, N.M.: Control strategies of an assistive robot using a Brain-Machine Interface. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3553–3558 (2012)Google Scholar
  45. 45.
    Úbeda, A., Iáñez, E., Azorín, J.M.: Shared control architecture based on RFID to control a robot arm using a spontaneous brain-machine interface. Robotics and Autonomous Systems 61(8), 768–774 (2012)CrossRefGoogle Scholar
  46. 46.
    Iáñez, E., Úbeda, A., Azorín, J.M., Perez-Vidal, C.: Assistive robot application based on an RFID control architecture and a wireless EOG interface. Robotics and Autonomous System 60(8), 1069–1077 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Enrique Hortal
    • 1
    Email author
  • Andrés Úbeda
    • 1
  • Eduardo Iáñez
    • 1
  • José M. Azorín
    • 1
  1. 1.Biomedical Neuroengineering Group in Miguel HernándezUniversity of ElcheElcheSpain

Personalised recommendations