Using Neurofeedback as an Alternative for Drug Therapy in Selected Mental Disorders

  • Zolubak Magda
  • Mariusz Pelc
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 720)


Neurofeedback is a very effective technique that may be considered as an alternative to drug therapy to support development of children with different mental disorders such as ADD. The aim of this paper is to show how neurofeedback can be used for therapeutic purposes to increase selected signal parameters. For the test purposes 30 therapeutic sessions with 13-year old boy are presented showing theta waves frequency and SMR increase in the frontal part of head and partial improvements in the central part. Another sessions lead to bringing the relevant signals frequencies near to desired, correct values. Overall outcome of the training has lead to increased ability to concentrate and to lengthening the time through which the patient was able to work without distractions.


Neurofeedback Mental therapy 


  1. 1.
    Begemann, M.J.H., Florisse, E.J.R., van Lutterveld, R., Kooyman, M., Sommer, I.E.: Efficacy of EEG neurofeedback in psychiatry: a comprehensive overview and meta-analysis. Transl. Brain Rhythm. 1(1), 19–29 (2016)CrossRefGoogle Scholar
  2. 2.
    Coben, R., Mohammad-Rezazadeh, I., Cannon, R.L.: Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: a theory of mixed over- and under-connectivity. Front. Hum. Neurosci. (2014)Google Scholar
  3. 3.
    Cowley, B., Holmström, E., Juurmaa, K., Kovarskis, L., Krause, C.M.: Computer enabled neuroplasticity treatment: a clinical trial of a novel design for neurofeedback therapy in adult ADHD. Front. Hum. Neurosci. (2016)Google Scholar
  4. 4.
    Franko, E., Wehner, T., Joly, O., Lowe, J., Porter, M., Kenny, J., Thompson, A., Rudge, P., Collinge, J., Mead, S.: Quantitative EEG parameters correlate with the progression of human prion diseases. J. Neurol. Neurosurg. Psychiatr. 87(10), 1061–1067 (2016)CrossRefGoogle Scholar
  5. 5.
    Geladé, K., Janssen, T., Bink, M., van Mourik, R.: Behavioral effects of neurofeedback compared to stimulants and physical activity in attention-deficit/hyperactivity disorder: a randomized controlled trial. J. Clin. Psychiatry 77(10), e1270–e1277 (2016)CrossRefGoogle Scholar
  6. 6.
    Heinrich, H., Strehl, U., Arns, M.: Neurofeedback and ADHD. Front. Hum. Neurosci. 9, 602 (2016)Google Scholar
  7. 7.
    Husain, A.M., Shina, S.R.: Continuous EEG Monitoring Principles and Practice. Springer, Cham (2017)CrossRefGoogle Scholar
  8. 8.
    Pop-Jordanova, N., Zorcec, T., Demerdzieva, A., Gucev, Z.: QEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder. Nonlinear Biomed. Phys. 4, 4 (2010)CrossRefGoogle Scholar
  9. 9.
    Keavy, D., Bristow, L.J., Sivarao, D.V., Batchelder, M., King, D., Thangathirupathy, S., Macor, J.E., Weed, M.R.: The QEEG signature of selective NMDA NR2B negative allosteric modulators; a potential translational biomarker for drug development. PLoS ONE 11(4), e0152729 (2016)CrossRefGoogle Scholar
  10. 10.
    Kubik, A.: Training Bio-feedback for Specialist and Therapist, vol. 2. Elmico Publishing House, Poland (2015). (in Polish)Google Scholar
  11. 11.
    Mohagheghi, A., Amiri, S., Bonab, N.M., Chalabianloo, G., Noorazar, S.G., Tabatabaei, S.M., Farhang, S.: A randomized trial of comparing the efficacy of two neurofeedback protocols for treatment of clinical and cognitive symptoms of ADHD: theta suppression/beta enhancement and theta suppression/alpha enhancement. BioMed Res. Int. 2017, 7 (2017)CrossRefGoogle Scholar
  12. 12.
    Moreno-García, I., Meneres-Sancho, S., de Rey, C.C.-V.: A randomized controlled trial to examine the posttreatment efficacy of neurofeedback, behavior therapy, and pharmacology on ADHD measures. J. Atten. Disorders (2017)Google Scholar
  13. 13.
    Pelc, M., Anthony, R.: Towards policy-based self-configuration of embedded systems. Syst. Inf. Sci. Notes 2(1), 20–26 (2007)Google Scholar
  14. 14.
    Reis, J., Portugal, A., Fernandes, L., Afonso, N., Pereira, M., Sousa, N., Dias, N.: An alpha and theta intensive and short neurofeedback protocol for healthy aging working-memory training. Front. Aging Neurosci. 8, 157 (2016)CrossRefGoogle Scholar
  15. 15.
    Skrap, M., Main, D., Ius, T., Fabbro, F., Tomasion, B.: Brain mapping: a novel intraoperative neuropsychological approach. J. Neurosurg. 125(4), 877–887 (2016)CrossRefGoogle Scholar
  16. 16.
    Trans Cranial Technologies: 10/20 System Positioning Manual, Hong Kong (2012)Google Scholar
  17. 17.
    Ward, P., Pelc, M., Hawthorne, J., Anthony, R.: Embedding dynamic behaviour into a self-configuring software system. In: Autonomic and Trusted Computing. LNCS, vol. 5060, pp. 373–387. Springer, Heidelberg (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Opole University of TechnologyOpolePoland
  2. 2.CIS DepartmentUniversity of GreenwichLondonUK

Personalised recommendations