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Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly

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Abstract

Neurofeedback training (NFT) has shown to be promising and useful to rehabilitate cognitive functions. Recently, brain–computer interfaces (BCIs) were used to restore brain plasticity by inducing brain activity with an NFT. In our study, we hypothesized that an NFT with a motor imagery-based BCI (MI-BCI) could enhance cognitive functions related to aging effects. To assess the effectiveness of our MI-BCI application, 63 subjects (older than 60 years) were recruited. This novel application was used by 31 subjects (NFT group). Their Luria neuropsychological test scores were compared with the remaining 32 subjects, who did not perform NFT (control group). Electroencephalogram changes measured by relative power (RP) endorsed cognitive potential findings under study: visuospatial, oral language, memory, intellectual and attention functions. Three frequency bands were selected to assess cognitive changes: 12, 18, and 21 Hz (bandwidth 3 Hz). Significant increases (p < 0.01) in the RP of these frequency bands were found. Moreover, results from cognitive tests showed significant improvements (p < 0.01) in four cognitive functions after performing five NFT sessions: visuospatial, oral language, memory, and intellectual. This established evidence in the association between NFT performed by a MI-BCI and enhanced cognitive performance. Therefore, it could be a novel approach to help elderly people.

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References

  1. Angelakis E, Stathopoulou S, Frymiare JL, Green DL, Lubar JL, Kounios J (2007) EEG neurofeedback: a brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clin Neuropsychol 21:110–129

    Article  PubMed  Google Scholar 

  2. Arns M, Heinrich H, Strehl U (2014) Evaluation of neurofeedback in ADHD: the long and winding road. Biol Psychol 95:108–115

    Article  PubMed  Google Scholar 

  3. Baker SN (2007) Oscillatory interactions between sensorimotor cortex and the periphery. Curr Opin Neurobiol 17(6):649–655

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bastiaansen M, Hagoort P (2006) Oscillatory neuronal dynamics during language comprehension. Prog Brain Res 159:179–196

    Article  PubMed  Google Scholar 

  5. Bauer RH (1976) Short-term memory: EEG alpha correlates and the effect of increased alpha. Behav Biol 17(4):425–433

    Article  CAS  PubMed  Google Scholar 

  6. Burke SN, Barnes CA (2006) Neural plasticity in the ageing brain. Nat Rev Neurosci 7:30–40

    Article  CAS  PubMed  Google Scholar 

  7. Chartock HE, Glassman PR, Poon LW, Marsh GR (1975) Changes in alpha rhythm asymmetry during learning of verbal and visuospatial tasks. Physiol Behav 15(2):237–239

    Article  CAS  PubMed  Google Scholar 

  8. Christensen AL (1979) A practical application of the Luria methodology. J Clin Exp Neuropsychol 1(3):241–247

    Article  Google Scholar 

  9. Clark L, Blackwell AD, Aron AR, Turner DC, Dowson J, Robbins TW, Sahakian BJ (2007) Association between response inhibition and working memory in adult ADHD: a link to right frontal cortex pathology? Biol Psychiatry 61(12):1395–1401

    Article  PubMed  Google Scholar 

  10. Coben R, Linden M, Myers TE (2010) Neurofeedback for autistic spectrum disorder: a review of the literature. Appl Psychophysiol Biofeedback 35:83–105

    Article  PubMed  Google Scholar 

  11. Craik FIM, Salthouse TA (2011) Handbook of aging and cognition II. Psychology Press, Hove

    Google Scholar 

  12. Curtis CE, D’Esposito M (2003) Persistent activity in the prefrontal cortex during working memory. Trends Cogn Sci 7(9):415–423

    Article  PubMed  Google Scholar 

  13. Daly JJ, Wolpaw JR (2008) Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 7(11):1032–1043

    Article  PubMed  Google Scholar 

  14. Doppelmayr M, Klimesch W, Stadler W, Pöllhuber D, Heine C (2002) EEG alpha power and intelligence. Intelligence 30(3):289–302

    Article  Google Scholar 

  15. Egner T, Gruzelier JH (2001) Learned selfregulation of EEG frequency components affects attention and event-related brain potentials in humans. Neuroreport 12(18):4155–4159

    Article  CAS  PubMed  Google Scholar 

  16. Egner T, Zech TF, Gruzelier JH (2004) The effects of neurofeedback training on the spectral topography of the electroencephalogram. Clin Neurophysiol 115(11):2452–2460

    Article  PubMed  Google Scholar 

  17. Gevensleben H, Holl B, Albrecht B, Vogel C, Schlamp D, Kratz O, Studer P, Rothenberger A, Moll GH, Heinrich H (2009) Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial. J Child Psychol Psychiatry 50(7):780–789

    Article  PubMed  Google Scholar 

  18. Ghaziri J, Tucholka A, Larue V, Blanchette-Sylvestre M, Reyburn G, Gilbert G, Lévesque J, Beauregard M (2013) Neurofeedback training induces changes in white and gray matter. Clin EEG Neurosci 44(4):265–272

    Article  PubMed  Google Scholar 

  19. Grady CL, Craik FI (2000) Changes in memory processing with age. Curr Opin Neurobiol 10(2):224–231

    Article  CAS  PubMed  Google Scholar 

  20. Guger C, Daban S, Sellers E, Holzner C, Krausz G, Carabalona R, Gramatica F, Edlinger G (2009) How many people are able to control a P300-based brain–computer interface (BCI)? Neurosci Lett 462:94–98

    Article  CAS  PubMed  Google Scholar 

  21. Güntekin B, Basar E (2007) Emotional face expressions are differentiated with brain oscillations. Int J Psychophysiol 64:91–100

    Article  PubMed  Google Scholar 

  22. Hadjidimitriou S, Zacharakis A, Doulgeris P, Panoulas K, Hadjileontiadis L, Panas S (2010) Sensorimotor cortical response during motion reflecting audiovisual stimulation: evidence from fractal EEG analysis. Med Biol Eng Comput 48(6):561–572

    Article  CAS  PubMed  Google Scholar 

  23. Hanslmayr S, Staudigl T, Fellner MC (2012) Oscillatory power decreases and long-term memory: the information via desynchronization hypothesis. Front Hum Neurosci 6:74

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4):411–430

    Article  PubMed  Google Scholar 

  25. Jasper HH (1958) Report of committee on methods of clinical examination in electroencephalography. Electroenceph Clin Neurophysiol 10:370–375

    Article  Google Scholar 

  26. Kim JS, Chung CK (2008) Language lateralization using MEG beta frequency desynchronization during auditory oddball stimulation with one-syllable words. Neuroimage 42(4):1499–1507

    Article  PubMed  Google Scholar 

  27. Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29(2):169–195

    Article  CAS  PubMed  Google Scholar 

  28. Lubar JO, Lubar JF (1984) Electroencephalographic biofeedback of SMR and beta for treatment of attention deficit disorders in a clinical setting. Biofeedback Self Regul 9(1):1–23

    Article  CAS  PubMed  Google Scholar 

  29. Lubar JF, Swartwood MO, Swartwood JN, O’Donnell PH (1995) Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in TOVA scores, behavioral ratings, and WISC-R performance. Biofeedback Self Regul 20(1):83–99

    Article  CAS  PubMed  Google Scholar 

  30. McCreadie KA, Coyle DH, Prasad G (2013) Sensorimotor learning with stereo auditory feedback for a brain–computer interface. Med Biol Eng Comput 51(3):285–293

    Article  PubMed  Google Scholar 

  31. Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G (2009) Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clin Neurophysiol 120(2):239–247

    Article  PubMed  Google Scholar 

  32. Pfurtscheller G, Neuper C (1997) Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239(2):65–68

    Article  CAS  PubMed  Google Scholar 

  33. Pineda JA, Silverman DS, Vankov A, Hestenes J (2003) Learning to control brain rhythms: making a brain–computer interface possible. IEEE Trans Neural Syst Rehabil Eng 11(2):181–184

    Article  PubMed  Google Scholar 

  34. Ros T, Munneke MA, Ruge D, Gruzelier JH, Rothwell JC (2010) Endogenous control of waking brain rhythms induces neuroplasticity in humans. Eur J Neurosci 31(4):770–778

    Article  PubMed  Google Scholar 

  35. Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR (2004) BCI2000: a general-purpose brain–computer interface (BCI) system. IEEE Trans Biomed Eng 51:1034–1043

    Article  PubMed  Google Scholar 

  36. Staufenbiel SM, Brouwer AM, Keizer AW, VanWouwe NC (2014) Effect of beta and gamma neurofeedback on memory and intelligence in the elderly. Biol Psychol 95:74–85

    Article  CAS  PubMed  Google Scholar 

  37. Tan G, Thornby J, Hammond DC, Strehl U, Canady B, Arnemann K, Kaiser DA (2009) Meta-analysis of EEG biofeedback in treating epilepsy. Clin EEG Neurosci 40(3):173–179

    Article  PubMed  Google Scholar 

  38. Thornton KE, Carmody DP (2008) Efficacy of traumatic brain injury rehabilitation: interventions of qEEG-guided biofeedback, computers, strategies, and medications. Appl Psychophysiol Biofeedback 33(2):101–124

    Article  PubMed  Google Scholar 

  39. United Nations (2013) Department of Economic and Social Affairs, Population Division 2013, World Population Ageing ST/ESA/SER.A/348. Available via DIALOG. http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf. Accessed 26 Jan 2015

  40. Vernon DJ (2005) Can neurofeedback training enhance performance? An evaluation of the evidence with implications for future research. Appl Psychophysiol Biofeedback 30(4):347–364

    Article  PubMed  Google Scholar 

  41. Vernon D, Egner T, Cooper N, Compton T, Neilands C, Sheri A, Gruzelier J (2003) The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int J Psychophysiol 47:75–85

    Article  PubMed  Google Scholar 

  42. Wang JR, Hsieh S (2013) Neurofeedback training improves attention and working memory performance. Clin Neurophysiol 124(12):2406–2420

    Article  PubMed  Google Scholar 

  43. Weiss S, Mueller HM (2012) Too many betas do not spoil the broth”: the role of beta brain oscillations in language processing. Front Psychol 3:201

    Article  PubMed  PubMed Central  Google Scholar 

  44. Welch PD (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73

    Article  Google Scholar 

  45. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8(2):164–173

    Article  CAS  PubMed  Google Scholar 

  46. 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  PubMed  Google Scholar 

  47. Young KD, Zotev V, Phillips R, Misaki M, Yuan H, Drevets WC, Bodurka J (2014) Real-time FMRI neurofeedback training of amygdala activity in patients with major depressive disorder. PLoS One 9(2):e88785. doi:10.1371/journal.pone.0088785

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zoefel B, Huster RJ, Herrmann CS (2011) Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage 54(2):1427–1431

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This research was supported in part by the Projects TEC2014-53196-R of ‘Ministerio de Economía y Competitividad’ and FEDER, the ‘Proyecto Cero’ 2011 on Ageing from Fundación General CSIC, Obra Social La Caixa and CSIC, and the Project VA059U13 of “Consejería de Educación”. Finally, J. Gomez-Pilar was in receipt of a PIF-UVA Grant from University of Valladolid.

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Authors and Affiliations

  1. Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011, Valladolid, Spain

    Javier Gomez-Pilar, Rebeca Corralejo, Luis F. Nicolas-Alonso, Daniel Álvarez & Roberto Hornero

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  1. Javier Gomez-Pilar
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  2. Rebeca Corralejo
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  3. Luis F. Nicolas-Alonso
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Correspondence to Javier Gomez-Pilar.

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Gomez-Pilar, J., Corralejo, R., Nicolas-Alonso, L.F. et al. Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly. Med Biol Eng Comput 54, 1655–1666 (2016). https://doi.org/10.1007/s11517-016-1454-4

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