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

AbstractNeurofeedback 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.

[1]  G. Pfurtscheller,et al.  Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface , 2009, Clinical Neurophysiology.

[2]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[3]  Thomas E. Myers,et al.  Neurofeedback for Autistic Spectrum Disorder: A Review of the Literature , 2010, Applied psychophysiology and biofeedback.

[4]  C. Barnes,et al.  Neural plasticity in the ageing brain , 2006, Nature Reviews Neuroscience.

[5]  David A. Kaiser,et al.  Meta-Analysis of EEG Biofeedback in Treating Epilepsy , 2009, Clinical EEG and neuroscience.

[6]  Sabine Weiss,et al.  “Too Many betas do not Spoil the Broth”: The Role of Beta Brain Oscillations in Language Processing , 2012, Front. Psychology.

[7]  S. Hanslmayr,et al.  Oscillatory power decreases and long-term memory: the information via desynchronization hypothesis , 2012, Front. Hum. Neurosci..

[8]  Kymberly D. Young,et al.  Real-Time fMRI Neurofeedback Training of Amygdala Activity in Patients with Major Depressive Disorder , 2014, PloS one.

[9]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[10]  Girijesh Prasad,et al.  Sensorimotor learning with stereo auditory feedback for a brain–computer interface , 2012, Medical & Biological Engineering & Computing.

[11]  Wolfgang Klimesch,et al.  EEG alpha power and intelligence , 2002 .

[12]  S. Baker.  Oscillatory interactions between sensorimotor cortex and the periphery , 2007, Current Opinion in Neurobiology.

[13]  D. Carmody,et al.  Efficacy of Traumatic Brain Injury Rehabilitation: Interventions of QEEG-guided Biofeedback, Computers, Strategies, and Medications , 2008, Applied psychophysiology and biofeedback.

[14]  Cheryl L Grady,et al.  Changes in memory processing with age , 2000, Current Opinion in Neurobiology.

[15]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[16]  P. Hagoort,et al.  Oscillatory neuronal dynamics during language comprehension. , 2006, Progress in brain research.

[17]  Erol Basar,et al.  Emotional face expressions are differentiated with brain oscillations. , 2007, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[18]  G. Pfurtscheller,et al.  Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.

[19]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[20]  M. Arns,et al.  Evaluation of neurofeedback in ADHD: The long and winding road , 2014, Biological Psychology.

[21]  J. Rothwell,et al.  Endogenous control of waking brain rhythms induces neuroplasticity in humans , 2010, The European journal of neuroscience.

[22]  J. Lubar,et al.  Electroencephalographic biofeedback of SMR and beta for treatment of attention deficit disorders in a clinical setting , 1984, Biofeedback and self-regulation.

[23]  J. Kounios,et al.  EEG Neurofeedback: A Brief Overview and an Example of Peak Alpha Frequency Training for Cognitive Enhancement in the Elderly , 2007, The Clinical neuropsychologist.

[24]  A practical application of the luria methodology , 1979 .

[25]  R. Bauer.  Short-term memory: EEG alpha correlates and the effect of increased alpha. , 1976, Behavioral biology.

[26]  Benedikt Zoefel,et al.  Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance , 2011, NeuroImage.

[27]  D. Vernon.  Can Neurofeedback Training Enhance Performance? An Evaluation of the Evidence with Implications for Future Research , 2005, Applied psychophysiology and biofeedback.

[28]  N. C. van Wouwe,et al.  Effect of beta and gamma neurofeedback on memory and intelligence in the elderly , 2014, Biological Psychology.

[29]  H. Jasper.  Report of the committee on methods of clinical examination in electroencephalography , 1958 .

[30]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[31]  S. Hsieh,et al.  Neurofeedback training improves attention and working memory performance , 2013, Clinical Neurophysiology.

[32]  J. Pineda,et al.  Learning to control brain rhythms: making a brain-computer interface possible , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  T. Egner,et al.  The effect of training distinct neurofeedback protocols on aspects of cognitive performance. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[34]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[35]  C. Curtis,et al.  Persistent activity in the prefrontal cortex during working memory , 2003, Trends in Cognitive Sciences.

[36]  Stavros M. Panas,et al.  Sensorimotor cortical response during motion reflecting audiovisual stimulation: evidence from fractal EEG analysis , 2010, Medical & Biological Engineering & Computing.

[37]  H. Heinrich,et al.  Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial. , 2009, Journal of child psychology and psychiatry, and allied disciplines.

[38]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[39]  F. Craik,et al.  The handbook of aging and cognition , 1992 .

[40]  Danielle C. Turner,et al.  Association Between Response Inhibition and Working Memory in Adult ADHD: A Link to Right Frontal Cortex Pathology? , 2007, Biological Psychiatry.

[41]  Alan Tucholka,et al.  Neurofeedback Training Induces Changes in White and Gray Matter , 2013, Clinical EEG and neuroscience.

[42]  June Sic Kim,et al.  Language lateralization using MEG beta frequency desynchronization during auditory oddball stimulation with one-syllable words , 2008, NeuroImage.

[43]  E. Sellers,et al.  How many people are able to control a P300-based brain–computer interface (BCI)? , 2009, Neuroscience Letters.

[44]  Changes in alpha rhythm asymmetry during learning of verbal and visuospatial tasks , 1975, Physiology & Behavior.

[45]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[46]  T. Egner,et al.  The effects of neurofeedback training on the spectral topography of the electroencephalogram , 2004, Clinical Neurophysiology.

[47]  J. Lubar,et al.  Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance , 1995, Biofeedback and self-regulation.

[48]  T. Egner,et al.  Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans , 2001, Neuroreport.