Applied Psychophysiology and Biofeedback

, Volume 36, Issue 1, pp 37–45 | Cite as

Predicting Successful Learning of SMR Neurofeedback in Healthy Participants: Methodological Considerations



Neurofeedback (NF) is a tool that has proven helpful in the treatment of various disorders such as epilepsy or attention deficit disorder (ADHD). Depending on the respective application, a high number of training sessions might be necessary before participants can voluntarily modulate the electroencephalographic (EEG) rhythms as instructed. In addition, many individuals never learn to do so despite numerous training sessions. Thus, we are interested in determining whether or not performance during the early training sessions can be used to predict if a participant will learn to regulate the EEG rhythms. Here, we propose an easy to use, but accurate method for predicting the performance of individual participants. We used a sample set of sensorimotor rhythm (SMR 12–15 Hz) NF training sessions (experiment 1) to predict the performance of the participants of another study (experiment 2). We then used the data obtained in experiment 2 to predict the performance of participants in experiment 1. We correctly predicted the performance of 12 out of 13 participants in the first group and all 14 participants in the second group; however, we were not able to make these predictions before the end of the eleventh training session.


Neurofeedback SMR Predicting performance 


  1. Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of Neurofeedback Treatment in ADHD: The Effects on Inattention, Impulsivity and Hyperactivity: A Meta-Analysis. EEG and Clinical Neuroscience, 40(3), 180–189.Google Scholar
  2. Dempster, T., & Vernon, D. (2009). Identifying indices of learning for alpha neurofeedback training. Applied Psychophysiology and Biofeedback, 34(4), 309–328.PubMedCrossRefGoogle Scholar
  3. Doehnert, M., Brandeis, D., Straub, M., Steinhausen, H. C., & Drechsler, R. (2008). Slow cortical potential neurofeedback in attention deficit hyperactivity disorder: Is there neurophysiological evidence for specific effects? Journal of Neural Transmission, 115(10), 1445–1456.PubMedCrossRefGoogle Scholar
  4. Doppelmayr, M., Nosko, H., Pecherstorfer, T., & Fink, A. (2007). An attempt to increase cognitive performance after stroke with Neurofeedback. Biofeedback, 35(4), 126–130.Google Scholar
  5. Egner, T., & Gruzelier, J. H. (2003). Ecological validity of neurofeedback: Modulation of slow wave EEG enhances musical performance. Neuroreport, 14(9), 1221–1224.PubMedCrossRefGoogle Scholar
  6. Egner, T., & Gruzelier, J. H. (2004). EEG Biofeedback of low beta band components: Frequency-specific effects on variables of attention and event-related brain potentials. Clinical Neurophysiology, 115(1), 131–139.PubMedCrossRefGoogle Scholar
  7. Egner, T., Strawson, E., & Gruzelier, J. H. (2002). EEG signature and phenomenology of alpha/theta neurofeedback training versus mock feedback. Applied Psychophysiology Biofeedback, 27(4), 261–270.CrossRefGoogle Scholar
  8. Gruzelier, J., Hardman, E., Wild, J., & Zaman, R. (1999). Learned control of slow potential interhemispheric asymmetry in schizophrenia. International Journal of Psychophysiology, 34(3), 341–348.PubMedCrossRefGoogle Scholar
  9. Hanslmayr, S., Sauseng, P., Doppelmayr, M., Schabus, M., & Klimesch, W. (2005). Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Applied Psychophysiology Biofeedback, 30(1), 1–10.CrossRefGoogle Scholar
  10. Hoedlmoser, K., Pecherstorfer, T., Gruber, G., Anderer, P., Doppelmayr, M., Klimesch, W., et al. (2008). Instrumental conditioning of human sensorimotor rhythm (12–15 Hz) and its impact on sleep as well as declarative learning. Sleep, 31(10), 1401–1408.PubMedGoogle Scholar
  11. Kotchoubey, B., Strehl, U., Uhlmann, C., Holzapfel, S., Koenig, M., Froescher, W., et al. (2001). Modification of slow cortical potentials in patients with refractory epilepsy: A controlled outcome study. Epilepsia, 42(3), 406–416.PubMedCrossRefGoogle Scholar
  12. Kouijzer, M. E. J., de Moor, J. M. H., Gerrits, B. J. L., Buitelaar, J. K., & van Schie, H. T. (in press). Long-term effects of neurofeedback treatment in autism. Research in Autism Spectrum Disorders, 3(2), 496–501.Google Scholar
  13. Kouijzer, M. E. J., de Moor, J. M. H., Gerrits, B. J. L., Congedo, M., & van Schie, H. T. (2009). Neurofeedback improves executive functioning in children with autism spectrum disorders. Research in Autism Spectrum Disorders, 3(1), 145–162.CrossRefGoogle Scholar
  14. Leins, U., Goth, G., Hinterberger, T., Klinger, Ch., Rumpf, N., & Strehl, U. (2007). Neurofeedback for Children with ADHD: A Comparison of SCP and Theta/Beta Protocols. Applied Psychophysiol Biofeedback, 32, 73–88.CrossRefGoogle Scholar
  15. Linden, M., Habib, T., & Radojevic, V. (1996). A controlled study of the effects of EEG biofeedback on the cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and Self-Regulation, 21(1), 35–48.PubMedCrossRefGoogle Scholar
  16. Pfurtscheller, G., & Lopes da Silva, F. (2005). EEG event-related desynchronization (ERD) and event-related sychronization (ERS). Electroencephalography and Clinical Neurophysiology, 5, 1003–1016.Google Scholar
  17. Raymond, J., Sajid, I., Parkinson, L. A., & Gruzelier, J. H. (2005a). Biofeedback and dance performance: A preliminary investigation. Applied Psychophysiology and Biofeedback, 30(1), 65–73.CrossRefGoogle Scholar
  18. Raymond, J., Varney, C., Parkinson, L. A., & Gruzelier, J. H. (2005b). The effects of alpha/theta neurofeedback on personality and mood. Cognitive Brain Research, 23(2–3), 287–292.PubMedCrossRefGoogle Scholar
  19. Rockstroh, B., Elbert, T., Birbaumer, N., Wolf, P., Duchting-Roth, A., Reker, M., et al. (1993). Cortical self-regulation in patients with epilepsies. Epilepsy Research, 14(1), 63–72.PubMedCrossRefGoogle Scholar
  20. Schenk, S., Lamm, K., & Ladwig, K. H. (2003). Effects of a neurofeedback-based alpha training on chronic tinnitus. Verhaltenstherapie, 13(2), 115–120.CrossRefGoogle Scholar
  21. Siniatchkin, M., Hierundar, A., Kropp, P., Kuhnert, R., Gerber, W. D., & Stephani, U. (2000). Self-regulation of slow cortical potentials in children with migraine: An exploratory study. Applied Psychophysiology Biofeedback, 25(1), 13–32.CrossRefGoogle Scholar
  22. Sterman, M. B., & Egner, T. (2006). Foundation and practice of neurofeedback for the treatment of epilepsy. Applied Psychophysiology Biofeedback, 31(1), 21–35.CrossRefGoogle Scholar
  23. Tan, G., Thornby, J., Hammond, D. C., Strehl, U., Canady, B., Arnemann, K., et al. (2009). Meta-analysis of EEG biofeedback in treating epilepsy. Clinical EEG and Neuroscience, 40(3), 173–179.PubMedGoogle Scholar
  24. Thompson, L., & Thompson, M. (2005). Neurofeedback intervention for adults with ADHD. Journal of Adult Development, 12(2–3), 123–130.CrossRefGoogle Scholar
  25. Thompson, M., & Thompson, L. (2009). Asperger’s syndrome intervention: Combining neurofeedback, biofeedback and metacognition Introduction to Quantitative EEG and Neurofeedback (Second Edition) (pp. 365–415). San Diego: Academic Press.Google Scholar
  26. Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., et al. (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. International Journal of Psychophysiology, 47(1), 75–85.PubMedCrossRefGoogle Scholar
  27. Walker, J. E., & Kozlowski, G. P. (2005). Neurofeedback treatment of epilepsy. Child and Adolescent Psychiatric Clinics of North America, 14(1), 163–176. doi:10.1016/j.chc.2004.07.009.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Physiological PsychologyUniversity of SalzburgSalzburgAustria

Personalised recommendations