Attention Training with an Easy–to–Use Brain Computer Interface

  • Filippo Benedetti
  • Nicola Catenacci Volpi
  • Leonardo Parisi
  • Giuseppe Sartori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8526)


This paper presents a cognitive training based on a brain–computer interface (BCI) that was developed for an adult subject with an attention disorder. According to the neurofeedback methodology, the user processes in real time his own electrical brain activity, which is detected through a non-invasive EEG device. The subject was trained in actively self modulating his own electrical patterns within a play therapy by using a reward–based virtual environment. Moreover, a consumer easy–to–use EEG headset was used, in order to assess its suitability for a concrete clinical application. At the end of the training, the patient obtained a significant improvement in attention.


Play therapy Attention training Rehabilitation Brain–computer interface (BCI) Neurofeedback 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ang, C.S., Sakel, M., Pepper, M., Phillips, M.: Use of brain–computer interfaces in neurological rehabilitation. British Journal of Neuroscience Nursing 7(3), 523–528 (2011)CrossRefGoogle Scholar
  2. 2.
    Brickenkamp, R., Zillmer, E.: The D2 test of attention. Hogrefe & Huber Pub. (1998)Google Scholar
  3. 3.
    Butnik, S.M.: Neurofeedback in adolescents and adults with attention deficit hyperactivity disorder. Journal of Clinical Psychology 61(5), 621–625 (2005)CrossRefGoogle Scholar
  4. 4.
    Conners, C.K., Epstein, J.N., Angold, A., Klaric, J.: Continuous performance test performance in a normative epidemiological sample. Journal of Abnormal Child Psychology 31(5), 555–562 (2003)CrossRefGoogle Scholar
  5. 5.
    Conners, C.K., Staff, M.H.S.: Conners’ continuous performance Test II (CPT II v. 5). Multi-Health Systems Inc., North Tonawanda (2000)Google Scholar
  6. 6.
    Davison, G.C., Neale, J.M.: Abnormal Psychology, Study Guide. Wiley Online Library, 117–119 (2000)Google Scholar
  7. 7.
    Dornhege, G.: Toward brain–computer interfacing. MIT press (2007)Google Scholar
  8. 8.
    Duvinage, M., Castermans, T., Dutoit, T., Petieau, M., Hoellinger, T., Saedeleer, C., Seetharaman, K., Cheron, G.: A P300-based quantitative comparison between the Emotiv Epoc headset and a medical EEG device. In: Proceedings of the IASTED International Conference Biomedical Engineering (2012)Google Scholar
  9. 9.
    Galán, F., Nuttin, M., Lew, E., Ferrez, P.W., Vanacker, G., Philips, J., del Millán, J.R.: A brain–actuated wheelchair: Asynchronous and non–invasive brain–computer interfaces for continuous control of robots. Clinical Neurophysiology 119(9), 2159–2169 (2008)CrossRefGoogle Scholar
  10. 10.
    Gevensleben, H., Holl, B., Albrecht, B., Schlamp, D., Kratz, O., Studer, P., Wangler, S., Rothenberger, A., Moll, G.H., Heinrich, H.: Distinct EEG effects related to neurofeedback training in children with ADHD: A randomized controlled trial. International Journal of Psychophysiology 74(2), 149–157 (2009)CrossRefGoogle Scholar
  11. 11.
    Jacobson, N.S., Truax, P.: Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology 59(1), 12 (1991)CrossRefGoogle Scholar
  12. 12.
    Jasper, H.H.: The ten twenty electrode system of the international federation. Electroencephalography and Clinical Neurophysiology 10, 371–375 (1958)Google Scholar
  13. 13.
    Kotchetkov, I.S., Hwang, B.Y., Appelboom, G., Kellner, C.P., Connolly Jr., E.S.: Brain–computer interfaces: Military, neurosurgical, and ethical perspective. Neurosurgical Focus 28(5), E25 (2010)Google Scholar
  14. 14.
    Lévesque, J., Beauregard, M., Mensour, B.: Effect of neurofeedback training on the neural substrates of selective attention in children with attention–deficit/hyperactivity disorder: A functional magnetic resonance imaging study. Neuroscience Letters 394(3), 216–221 (2006)CrossRefGoogle Scholar
  15. 15.
    del Millán, J.R., Rupp, R., Müller–Putz, G.R., Murray–Smith, R., Giugliemma, C., Tangermann, M., Vidaurre, C., Cincotti, F., Kübler, A., Leeb, R., et al.: Combining brain–computer interfaces and assistive technologies: State–of–the–art and challenges. Frontiers in Neuroscience 4 (2010)Google Scholar
  16. 16.
    Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R.: Brain–computer interfaces for control of neuroprostheses: From synchronous to asynchronous mode of operation / Brain–computer interfaces zur steuerung von neuroprothesen: von der synchronen zur asynchronen funktionsweise. Biomedizinische Technik 51(2), 57–63 (2006)CrossRefGoogle Scholar
  17. 17.
    Neumann, N., Kuübler, A., Kaiser, J., Hinterberger, T., Birbaumer, N.: Conscious perception of brain states: Mental strategies for brain–computer communication. Neuropsychologia 41(8), 1028–1036 (2003)CrossRefGoogle Scholar
  18. 18.
    Nijholt, A., Bos, D.P.O., Reuderink, B.: Turning shortcomings into challenges: Brain–computer interfaces for games. Entertainment Computing 1(2), 85–94 (2009)CrossRefGoogle Scholar
  19. 19.
    Posner, M.I.: Orienting of attention. Quarterly Journal of Experimental Psychology 32(1), 3–25 (1980)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Van Aart, J., Klaver, E.R., Bartneck, C., Feijs, L.M., Peters, P.J.: EEG headset for neurofeedback therapy enabling easy use in the home environment. Citeseer (2008)Google Scholar
  21. 21.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J., Vaughan, T.M., et al.: Brain–computer interface technology: A review of the first international meeting. IEEE Transactions on Rehabilitation Engineering 8(2), 164–173 (2000)CrossRefGoogle Scholar
  22. 22.
    Yaomanee, K., Pan-ngum, S., Ayuthaya, P.I.N.: Brain signal detection methodology for attention training using minimal EEG channels. In: 2012 10th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering), pp. 84–89. IEEE (2012)Google Scholar
  23. 23.
    Bondoc, S., Powers, C., Herz, N., Hermann, V.: Virtual Reality-Based Rehabilitation. OT Practice 15(11) (2010)Google Scholar
  24. 24.
    Harris, K., Reid, D.: The influence of virtual reality play on children’s motivation. Canadian Journal of Occupational Therapy 72(1), 21–29 (2005)CrossRefGoogle Scholar
  25. 25.
    Cho, B., Ku, J., Pyojang, D., Kim, S., Lee, Y.H., Kim, I.Y., et al.: The effect of virtual reality cognitive training for attention enhancement. CyberPsychology and Behaviour 5(2), 129–137 (2002)CrossRefGoogle Scholar
  26. 26.
    Emotiv EPOC Research Edition SDK, (accessed October 2, 2014)
  27. 27.
    NeuroSky, (accessed October 2, 2014)
  28. 28.
    Rand, D., Kizony, R., Weiss, P.L.: Virtual reality rehabilitation for all: Vivid GX versus Sony PlayStation II EyeToy. In: 5th Intl. Conf. On Disability, Virtual Environments and Assoc. Technologies, pp. 87–94 (2004)Google Scholar
  29. 29.
    Halton, J.: Virtual rehabilitation with video games: A new frontier for occupational therapy. Occupational Therapy Now 9(6), 12–14 (2008)Google Scholar
  30. 30.
    Aart, J.V., Klaver, E., Bartneck, C., Feijs, L., Peters, P.: EEG Headset For Neurofeedback Therapy - Enabling Easy Use in the Home Environment. In: Proceedings of the Biosignals -. International Conference on Bio-inspired Signals and Systems, Funchal, pp. 23–30 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Filippo Benedetti
    • 1
  • Nicola Catenacci Volpi
    • 2
  • Leonardo Parisi
    • 3
    • 4
  • Giuseppe Sartori
    • 1
  1. 1.Department of General PsychologyPadova UniversityPadovaItaly
  2. 2.Computer Science DepartmentUniv. of HertfordshireHatfieldUK
  3. 3.UOS SapienzaIstituto Sistemi Complessi, CNRRomeItaly
  4. 4.Dipartimento di InformaticaUniversità La SapienzaRomeItaly

Personalised recommendations