Advertisement

Testing the Specificity of EEG Neurofeedback Training on First- and Second-Order Measures of Attention

  • Eddy J. DavelaarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10284)

Abstract

During electroencephalography (EEG) neurofeedback training, individuals learn to willfully modulate their brain oscillations. Successful modulation has been shown to be related to cognitive benefits and wellbeing. The current paper addresses the specificity of three neurofeedback protocols in influencing first- (basic Stroop effect) and second-order (Gratton effect) measures of attentional control. The data come from two previously presented studies that included the Stroop task to assess attentional control. The three neurofeedback protocols were upregulation of frontal alpha, sensorimotor (SMR), and mid-frontal theta oscillations. The results show specific effects of different EEG neurofeedback protocols on attentional control and are modulated by the cognitive effort needed in the Stroop task. To summarize, in less-demanding versions of the Stroop task, alpha training improves first- and second-order attentional control, whereas SMR and theta training had no effect. In the demanding version of the Stroop task, theta training improves first-order, but not second-order control and SMR training has no effect on either. Using a drift diffusion model-based analysis, it is shown that only alpha and theta training modulate the underlying cognitive processing, with theta upregulation enhancing evidence accumulation. Although the current results need to be interpreted with caution, they support the use of different neurofeedback protocols to augment specific aspects of the attentional system. Recommendations for future work are made.

Keywords

EEG neurofeedback Stroop effect Gratton effect Attention training 

Notes

Acknowledgements

I thank my co-authors on the two studies (in alphabetical order), Soma Almasi, Joe Barnby, Anna Berger, Virginia Eatough, Emily Hickson, Natasha Kevat, and Sonny Ramtale. Parts of this research was supported by a Faculty of Science Research grant and an ISSF grant to E.J. Davelaar and V. Eatough.

References

  1. 1.
    Arns, M., de Ridder, S., Strehl, U., Breteler, M., Coenen, A.: Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin. EEG Neurosci. 40, 180–189 (2009)CrossRefGoogle Scholar
  2. 2.
    Schabus, M., Heib, D.P.J., Lechinger, J., Griessenberger, H., Klimesch, W., Pawlizki, A., Kunz, A.B., Sterman, B.M., Hoedlmoser, K.: Enhancing sleep quality and memory in insomnia using instrumental sensorimotor rhythm conditioning. Biol. Psychol. 95, 126–134 (2014)CrossRefGoogle Scholar
  3. 3.
    Scott, W.C., Kaiser, D., Othmer, S., Sideroff, S.I.: Effects of an EEG biofeedback protocol on a mixed substance abusing population. Am. J. Drug Alcohol Abuse 31, 455–469 (2005)CrossRefGoogle Scholar
  4. 4.
    Tan, G., Thornby, J., Hammond, D.C., Strehl, U., Canady, B., Arnemann, K., Kaiser, D.A.: Meta-analysis of EEG biofeedback in treating epilepsy. Clin. EEG Neurosci. 40, 173–179 (2009)CrossRefGoogle Scholar
  5. 5.
    Gruzelier, J.H.: EEG-neurofeedback for optimising performance. I: a review of cognitive and affective outcome in healthy participants. Neurosci. Biobehav. Rev. 44, 124–141 (2014)CrossRefGoogle Scholar
  6. 6.
    Gruzelier, J.H.: EEG-neurofeedback for optimising performance. II: creativity, the performing arts and ecological validity. Neurosci. Biobehav. Rev. 44, 142–158 (2014)CrossRefGoogle Scholar
  7. 7.
    Klimesch, W.: EEG Alpha and Theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29, 169–195 (1999)CrossRefGoogle Scholar
  8. 8.
    Nan, W., Rodrigues, J.P., Ma, J., Qu, X., Wan, F., Mak, P.-I., Mak, P.U., Vai, M.I., Rosa, A.: Individual Alpha neurofeedback training effect on short term memory. Int. J. Psychophysiol. 86, 83–87 (2012)CrossRefGoogle Scholar
  9. 9.
    Botvinick, M.M., Braver, T.S., Barch, D.M., Carter, C.S., Cohen, J.D.: Conflict monitoring and cognitive control. Psychol. Rev. 108(3), 624–652 (2001)CrossRefGoogle Scholar
  10. 10.
    Botvinick, M.M., Cohen, J.D., Carter, C.S.: Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn. Sci. 8, 539–546 (2004)CrossRefGoogle Scholar
  11. 11.
    Davelaar, E.J., Berger, A.: Enhanced reactive cognitive control through virtual reality EEG neurofeedback. Front. Hum. Neurosci. (2016). doi: 10.3389/conf.fnhum.2016.220.00045. Conference Abstract: SAN2016 Meeting
  12. 12.
    Gruzelier, J.H.: EEG-neurofeedback for optimising performance. III: a review of methodological and theoretical considerations. Neurosci. Biobehav. Rev. 44, 159–182 (2014)CrossRefGoogle Scholar
  13. 13.
    Davelaar, E.J., Eatough, V., Almasi, S., Barnby, J.M., Hickson, E., Kevat, N., Ramtale, C.: Neurofeedback training and cognitive performance: a pilot study using an integrated cognitive and phenomenological approach. Front. Hum. Neurosci. (2016). doi: 10.3389/conf.fnhum.2016.220.00033. Conference Abstract: SAN2016 Meeting
  14. 14.
    Ratcliff, R., McKoon, G.: The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 20, 873–922 (2008)CrossRefzbMATHGoogle Scholar
  15. 15.
    Wagenmakers, E.-J., van der Maas, H.L.J., Grasman, R.P.P.P.: An EZ-diffusion model for response time and accuracy. Psychon. Bull. Rev. 14, 3–22 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Psychological Sciences, Birkbeck CollegeUniversity of LondonLondonUK

Personalised recommendations