Neurofeedback Training with a Motor Imagery-Based BCI Improves Neurocognitive Functions in Elderly People

  1. Gomez-Pilar, Javier 1
  2. Corralejo, Rebeca 1
  3. Álvarez, Daniel 1
  4. Hornero, Roberto 1
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Livre:
SpringerBriefs in Electrical and Computer Engineering

ISSN: 2191-8112,  2191-8120

ISBN: 9783319251882,  9783319251905

Année de publication: 2015

Pages: 43-55

Type: Chapitre d'ouvrage

DOI: 10.1007/978-3-319-25190-5_5 GOOGLE SCHOLAR lock_openAccès ouvert editor

Objectifs de Développement Durable

Résumé

In recent years, brain-computer interfaces (BCIs) have become not only a tool to provide communication and control for people with disabilities, but also a way to rehabilitate some motor or cognitive functions. Brain plasticity can help restore normal brain functions by inducing brain activity. In fact, voluntary event-related desynchronization (ERD) in upper alpha and beta electroencephalogram (EEG) activity bands is associated with different neurocognitive functions. In this regard, neurofeedback training (NFT) has shown to be a suitable way to control one’s own brain activity. Furthermore, new evidence in recent studies showed NFT could lead to microstructural changes in white and grey matter. In our novel study, NFT qualities were applied to aging-related effects. We hypothesized that a NFT by means of motor imagery-based BCI (MI-BCI) could affect different cognitive functions in elderly people. To assess the effectiveness of this application, we studied 63 subjects, all above 60 years old. The subjects were divided into a control group (32 subjects) and a NFT group (31 subjects). To validate the effectiveness of the NFT using MI-BCI, variations in the scores of neuropsychological tests (Luria tests) were measured and analyzed. Results showed significant improvements (p < 0.05) in the NFT group, after only five NFT sessions, in four cognitive functions: visuospatial; oral language; memory; and intellectual. These results further support the association between NFT and the enhancement of cognitive performance. Findings showed the potential usefulness of NFT using MI-BCI. Therefore, this approach could lead to new means to help elderly people.

Références bibliographiques

  • F.I.M Craik, T.A Salthouse, Handbook of Aging and Cognition II. (Psychology Press, United Kingdom, 2011)
  • A.L. Christensen, A practical application of the Luria methodology. J. Clin. Exp. Neuropsychol. 1(3), 241–247 (1979)
  • United Nations, Department of Economic and Social Affairs, Population Division (2013). World Population Ageing 2013. ST/ESA/SER.A/348, http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf . Accessed 20 Nov 2014
  • J.J. Daly, J.R. Wolpaw, Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 7(11), 1032–1043 (2008)
  • M. Arns, H. Heinrich, U. Strehl, Evaluation of neurofeedback in ADHD: the long and winding road. Biol. Psychol. 95, 108–115 (2014)
  • M.B. Sterman, T. Egner, Foundation and practice of neurofeedback for the treatment of epilepsy. Appl. Psychophysiol. Biofeedback 31, 21–35 (2006)
  • R. Coben, M. Linden, T.E. Myers, Neurofeedback for autistic spectrum disorder: a review of the literature. Appl. Psychophysiol. Biofeedback 35, 83–105 (2010)
  • K.E. Thornton, D.P. Carmody, Efficacy of traumatic brain injury rehabilitation: Interventions of qEEG-guided biofeedback, computers, strategies, and medications. Appl. Psychophysiol. Biofeedback 33(2), 101–124 (2008)
  • D. Vernon, T. Egner, N. Cooper, T. Compton, C. Neilands, A. Sheri, J. Gruzelier, The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int. J. Psychophysiol. 47, 75–85 (2003)
  • S.M. Staufenbiel, A.M. Brouwer, A.W. Keizer, N.C. VanWouwe, Effect of beta and gamma Neurofeedback on memory and intelligence in the elderly. Biol. Psychol. 95, 74–85 (2014)
  • J.R. Wang, S. Hsieh, Neurofeedback training improves attention and working memory performance. Clin. Neurophysiol. 124(12), 2406–2420 (2013)
  • E. Angelakis, S. Stathopoulou, J.L. Frymiare, D.L. Green, J.L. Lubar, J. Kounios, EEG neurofeedback: a brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clin. Neuropsychol. 21, 110–129 (2006)
  • T. Ros, M.A. Munneke, D. Ruge, J.H. Gruzelier, J.C. Rothwell, Endogenous control of waking brain rhythms induces neuroplasticity in humans. Eur. J. Neurosci. 31(4), 770–778 (2010)
  • J. Ghaziri, A. Tucholka, V. Larue, M. Blanchette-Sylvestre, G. Reyburn, G. Gilbert, J. Lévesque, M. Beauregard, Neurofeedback training induces changes in white and gray matter. Clin. EEG Neurosci. 44(4), 265–272 (2013)
  • H.H. Jasper, Report of committee on methods of clinical examination in electroencephalography. Electroenceph. Clin. Neurophysiol. 10, 370–375 (1958)
  • G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, J.R. Wolpaw, BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)
  • J.A. Pineda, D.S. Silverman, A. Vankov, J. Hestenes, Learning to control brain rhythms: making a brain-computer interface possible. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 181–184 (2003)
  • J.A. Pineda, E.V. Friedrich, K. LaMarca, Neurorehabilitation of social dysfunctions: a model-based neurofeedback approach for low and high-functioning autism. Front. Neuroeng. 7, 29 (2014)
  • B. Güntekin, E. Basar, Emotional face expressions are differentiated with brain oscillations. Int. J. Psychophysiol. 64, 91–100 (2007)