Neurofeedback

Neurofeedback is a type of biofeedback that focuses on the neuronal activity of the brain. The training method is based on reinforcement learning, where real-time feedback provided to the trainee is supposed to reward and reinforce desired brain activity or inhibit unfavorable activity patterns. In short, it is self-regulating training that generally utilizes EEG for operant conditioning.

Neurofeedback training process diagram

Different mental states (for example, concentration, relaxation, creativity, distractibility, rumination, etc.) are associated with different brain activities or brain states.

Similarly, symptoms of mental or brain-related health issues are associated with neuronal overarousal, underarousal, disinhibition, or instability. Thus, neurofeedback tries to yield symptom relief through an improved regulation of neuronal activity.  

Apart from being a therapeutic approach, neurofeedback is increasingly used for healthy people as well, aiming at improved cognitive regulation skills according to individual goals and needs.

There are various methods of providing feedback of neurological activity. The most common application uses the measurement of electroencephalography (EEG), where the electrical activity of the brain is recorded by electrodes placed on the scalp. Other, less usual methods, rely on functional magnetic resonance (fMRI), functional near-infrared spectroscopy (fNIRS), or hemoencephalography biofeedback (HEG).

History

In 1898, Edward Thorndike formulated the law of effect. In his work, he theorized that behavior is shaped by satisfying or discomforting consequences. This set the foundation for operant conditioning.

In 1924, the German psychiatrist Hans Berger connected several electrodes to a patient's scalp and detected a small current by using a ballistic galvanometer. In his subsequent studies, Berger analyzed EEGs qualitatively, but in 1932, G. Dietsch applied Fourier analysis to seven EEG records and later became the first researcher to apply quantitative EEG (QEEG).

In 1950, Neal E. Miller of Yale University was able to train mice to regulate their heartbeat frequency. Later on, he continued his work with humans, training them through auditory feedback.[1]

The first study to demonstrate neurofeedback was reported by Joe Kamiya in 1962.[2][3] Kamiya's experiment had two parts: In the first part, a subject was asked to keep their eyes closed, and when a tone sounded, to say whether they were experiencing alpha waves. Initially, the subject would guess correctly about fifty percent of the time, but some subjects would eventually develop the ability to better distinguish between states.[4]

M. Barry Sterman trained cats to modify their EEG patterns to exhibit more of the so-called sensorimotor rhythm (SMR). He published this research in 1967. Sterman subsequently discovered that the SMR-trained cats were much more resistant to epileptic seizures after exposure to the convulsant chemical monomethylhydrazine than non-trained cats.[5] In 1971, he reported similar improvements with an epileptic patient whose seizures could be controlled through SMR training.[6] Joel Lubar contributed to the research of EEG biofeedback, starting with epilepsy[7] and later with hyperactivity and ADHD.[8]

Neuroplasticity

In 2010, a study provided some evidence of neuroplastic changes occurring after brainwave training. In this study, half an hour of voluntary control of brain rhythms led to a lasting shift in cortical excitability and intracortical function.[9] The authors observed that the cortical response to transcranial magnetic stimulation (TMS) was significantly enhanced after neurofeedback, persisted for at least twenty minutes, and was correlated with an EEG time-course indicative of activity-dependent plasticity[9]

Types of neurofeedback

The term neurofeedback is not legally protected. There are various approaches that give feedback about neuronal activity, and as such are referred to as "neurofeedback" by their respective operators. Distinctions can be made on several levels. The first takes into account which technology is being used (EEG,[10][11][12][13][14] fMRI,[15][16][17][18] fNIRS,[19] HEG). Nonetheless, further distinctions are crucial even within the realm of EEG neurofeedback, as different methodologies of analysis can be chosen, some of which are backed up by a higher number of peer-reviewed studies, whereas for others, scientific literature is scarce, and explanatory models are entirely missing.

Despite these differences, a common denominator can be found in the requirement of providing feedback. Usually, feedback is provided by auditory or visual input. While original feedback was provided by sounding tones according to neurological activity, many new ways have been found. It is possible to listen to music or podcasts where the volume is controlled as feedback, for example. Often, visual feedback is used in the form of animations on a TV screen. Visual feedback can also be provided in combination with videos and films, or even during reading tasks where the brightness of the screen represents the direct feedback. Simple games can also be used, where the game itself is controlled by the brain activity. Recent developments have tried to incorporate virtual reality (VR), and controllers can already be used for more involved engagement with the feedback.

Frequency band / amplitude training

Amplitude training, or frequency band training (used synonymously), is the method with the largest body of scientific literature; it also represents the original method of EEG neurofeedback.[2][6][8] The EEG signal is analyzed with respect to its frequency spectrum, split into the common frequency bands used in EEG neuroscience (delta, theta, alpha, beta, gamma). The activity involves training the amplitude of a certain frequency band on a defined location on the scalp to higher or lower values.

Depending on the training goal (for example, increasing attention and focus,[20][21] reaching a calm state,[22] reducing epileptic seizures,[6][23][24] etc.), the electrodes have to be placed in different positions. Additionally, the trained frequency bands and the training directions (to higher or lower amplitudes) might vary according to the training goal.

Thus, EEG wave components that are expected to be beneficial to the training goal are rewarded with positive feedback when appearing and/or increasing in amplitude. Frequency band amplitudes that are expected to be hindering are trained downwards by reinforcement through the feedback.

As an example, considering ADHD, this would result in training low-beta or mid-beta frequencies in the central-to-frontal lobe to increase in amplitude, while simultaneously trying to reduce theta and high-beta amplitudes in the same region of the brain.[25][26][27]

SCP training

For SCP (slow cortical potentials) training, one trains the DC voltage component of the EEG signal. The application of this type of EEG neurofeedback training was mostly endorsed by research done by Niels Birbaumer and his group. The most common symptom base for SCP training is ADHD, whereas SCPs also find their application in brain-computer interfaces.[28]

LORETA (low resolution electromagnetic tomography analysis) training

Normal EEG signals are restricted to the surface of the scalp. Using a high number of electrodes (19 or more), the source of certain electrical events can be localized. Similar to a tomography that renders a 3D image out of many 2D images, the many EEG channels are used to create LORETA images that represent in 3D the electrical activity distribution within the brain. The LORETA method can be used in combination with MRI to merge structural and functional activities. It is able to provide even better temporal resolution than PET or fMRI. For the application with live neurofeedback, however, 19-channel neurofeedback and LORETA has limited scientific evidence, and until now, shows no benefit over traditional 1- or 2-channel neurofeedback.[29]

Discussion and critique

There is ongoing discussion about the effect size of neurofeedback in the scientific literature. As neurofeedback is explained mostly based on the model of operant conditioning, the sensitivity of the feedback (the difficulty to receive a reward) also plays a role. It has been shown that the desired conditioning can be reversed if threshold values are set too low.[30] Other publications have not found any effect of neurofeedback, apart from placebo, when using automatic thresholds that update every thirty seconds in order to maintain a constant success rate of 80%.[31][32]

See also

References
  1. Pickering, T. G.; Miller, N. E. (1 September 1975). "Learned Voluntary Control of Heart Rate and Rhythm in Two Subjects with Premature Ventricular Contractions". Clinical Science. 49 (3): 17P–18P. doi:10.1042/cs049017Pd. ISSN 0301-0538.
  2. Kamiya, Joe (1979), "Autoregulation of the EEG Alpha Rhythm: A Program for the Study of Consciousness", Mind/Body Integration, Boston, MA: Springer US, pp. 289–297, doi:10.1007/978-1-4613-2898-8_25, ISBN 978-1-4613-2900-8, retrieved 28 April 2023
  3. Kamiya, Joe (22 February 2011). "The First Communications About Operant Conditioning of the EEG". Journal of Neurotherapy. 15 (1): 65–73. doi:10.1080/10874208.2011.545764. ISSN 1087-4208.
  4. Frederick, Jon A. (September 2012). "Psychophysics of EEG alpha state discrimination". Consciousness and Cognition. 21 (3): 1345–1354. doi:10.1016/j.concog.2012.06.009. PMC 3424312. PMID 22800733.
  5. Sterman, M. Barry (January 2000). "Basic Concepts and Clinical Findings in the Treatment of Seizure Disorders with EEG Operant Conditioning". Clinical Electroencephalography. 31 (1): 45–55. doi:10.1177/155005940003100111. ISSN 0009-9155. PMID 10638352. S2CID 43506749.
  6. Sterman, M.B; Friar, L (July 1972). "Suppression of seizures in an epileptic following sensorimotor EEG feedback training". Electroencephalography and Clinical Neurophysiology. 33 (1): 89–95. doi:10.1016/0013-4694(72)90028-4. PMID 4113278.
  7. Seifert, A.R.; Lubar, J.F. (November 1975). "Reduction of epileptic seizures through EEG biofeedback training". Biological Psychology. 3 (3): 157–184. doi:10.1016/0301-0511(75)90033-2. PMID 812560. S2CID 15698128.
  8. Lubar, Joel F.; Shouse, Margaret N. (September 1976). "EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR): A preliminary report". Biofeedback and Self-Regulation. 1 (3): 293–306. doi:10.1007/BF01001170. ISSN 0363-3586. PMID 990355. S2CID 17141352.
  9. Ros T, Munneke MA, Ruge D, Gruzelier JH, Rothwell JC (February 2010). "Endogenous control of waking brain rhythms induces neuroplasticity in humans". The European Journal of Neuroscience. 31 (4): 770–8. doi:10.1111/j.1460-9568.2010.07100.x. PMID 20384819. S2CID 16969327.
  10. Lubar, Joel F.; Swartwood, Michie Odle; Swartwood, Jeffery N.; O'Donnell, Phyllis H. (1 March 1995). "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". Biofeedback and Self-Regulation. 20 (1): 83–99. doi:10.1007/BF01712768. ISSN 1573-3270. PMID 7786929. S2CID 19193823.
  11. Kluetsch, R. C.; Ros, T.; Théberge, J.; Frewen, P. A.; Calhoun, V. D.; Schmahl, C.; Jetly, R.; Lanius, R. A. (August 2014). "Plastic modulation of PTSD resting-state networks and subjective wellbeing by EEG neurofeedback". Acta Psychiatrica Scandinavica. 130 (2): 123–136. doi:10.1111/acps.12229. PMC 4442612. PMID 24266644.
  12. Reiter, Karen; Andersen, Søren Bo; Carlsson, Jessica (February 2016). "Neurofeedback Treatment and Posttraumatic Stress Disorder: Effectiveness of Neurofeedback on Posttraumatic Stress Disorder and the Optimal Choice of Protocol". Journal of Nervous & Mental Disease. 204 (2): 69–77. doi:10.1097/NMD.0000000000000418. ISSN 0022-3018. PMID 26825263. S2CID 25210316.
  13. Micoulaud-Franchi, Jean-Arthur; Geoffroy, Pierre Alexis; Fond, Guillaume; Lopez, Régis; Bioulac, Stéphanie; Philip, Pierre (2014). "EEG neurofeedback treatments in children with ADHD: an updated meta-analysis of randomized controlled trials". Frontiers in Human Neuroscience. 8: 906. doi:10.3389/fnhum.2014.00906. ISSN 1662-5161. PMC 4230047. PMID 25431555.
  14. Omejc, Nina; Rojc, Bojan; Battaglini, Piero Paolo; Marusic, Uros (20 November 2018). "Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback". Bosnian Journal of Basic Medical Sciences. 19 (3): 213–220. doi:10.17305/bjbms.2018.3785. ISSN 1840-4812. PMC 6716090. PMID 30465705.
  15. Zotev, Vadim; Phillips, Raquel; Yuan, Han; Misaki, Masaya; Bodurka, Jerzy (15 January 2014). "Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback". NeuroImage. Neuro-enhancement. 85: 985–995. arXiv:1301.4689. doi:10.1016/j.neuroimage.2013.04.126. ISSN 1053-8119. PMID 23668969. S2CID 2836232.
  16. Pindi, Pamela; Houenou, Josselin; Piguet, Camille; Favre, Pauline (December 2022). "Real-time fMRI neurofeedback as a new treatment for psychiatric disorders: A meta-analysis". Progress in Neuro-Psychopharmacology and Biological Psychiatry. 119: 110605. doi:10.1016/j.pnpbp.2022.110605. PMID 35843369. S2CID 250586279.
  17. Linhartová, Pavla; Látalová, Adéla; Kóša, Barbora; Kašpárek, Tomáš; Schmahl, Christian; Paret, Christian (June 2019). "fMRI neurofeedback in emotion regulation: A literature review". NeuroImage. 193: 75–92. doi:10.1016/j.neuroimage.2019.03.011. PMID 30862532. S2CID 72333597.
  18. Nicholson, Andrew A.; Rabellino, Daniela; Densmore, Maria; Frewen, Paul A.; Paret, Christian; Kluetsch, Rosemarie; Schmahl, Christian; Théberge, Jean; Neufeld, Richard W.J.; McKinnon, Margaret C.; Reiss, Jeffrey P.; Jetly, Rakesh; Lanius, Ruth A. (January 2017). "The neurobiology of emotion regulation in posttraumatic stress disorder: Amygdala downregulation via real‐time fMRI neurofeedback". Human Brain Mapping. 38 (1): 541–560. doi:10.1002/hbm.23402. ISSN 1065-9471. PMC 6866912. PMID 27647695.
  19. Kohl, Simon H.; Mehler, David M. A.; Lührs, Michael; Thibault, Robert T.; Konrad, Kerstin; Sorger, Bettina (21 July 2020). "The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback—A Systematic Review and Recommendations for Best Practice". Frontiers in Neuroscience. 14: 594. doi:10.3389/fnins.2020.00594. ISSN 1662-453X. PMC 7396619. PMID 32848528.
  20. Arns, Martijn; Clark, C. Richard; Trullinger, Mark; deBeus, Roger; Mack, Martha; Aniftos, Michelle (June 2020). "Neurofeedback and Attention-Deficit/Hyperactivity-Disorder (ADHD) in Children: Rating the Evidence and Proposed Guidelines". Applied Psychophysiology and Biofeedback. 45 (2): 39–48. doi:10.1007/s10484-020-09455-2. ISSN 1090-0586. PMC 7250955. PMID 32206963.
  21. Van Doren, Jessica; Arns, Martijn; Heinrich, Hartmut; Vollebregt, Madelon A.; Strehl, Ute; K. Loo, Sandra (March 2019). "Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis". European Child & Adolescent Psychiatry. 28 (3): 293–305. doi:10.1007/s00787-018-1121-4. ISSN 1018-8827. PMC 6404655. PMID 29445867.
  22. Krylova, Marina; Skouras, Stavros; Razi, Adeel; Nicholson, Andrew A.; Karner, Alexander; Steyrl, David; Boukrina, Olga; Rees, Geraint; Scharnowski, Frank; Koush, Yury (3 December 2021). "Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks". Scientific Reports. 11 (1): 23363. doi:10.1038/s41598-021-02079-4. ISSN 2045-2322. PMC 8642545. PMID 34862407.
  23. Sterman, M. Barry; Egner, Tobias (March 2006). "Foundation and Practice of Neurofeedback for the Treatment of Epilepsy". Applied Psychophysiology and Biofeedback. 31 (1): 21–35. doi:10.1007/s10484-006-9002-x. ISSN 1090-0586. PMID 16614940. S2CID 1445660.
  24. Monderer, Renee S; Harrison, Daniel M; Haut, Sheryl R (June 2002). "Neurofeedback and epilepsy". Epilepsy & Behavior. 3 (3): 214–218. doi:10.1016/S1525-5050(02)00001-X. PMID 12662600. S2CID 31198834.
  25. Van Doren, Jessica; Arns, Martijn; Heinrich, Hartmut; Vollebregt, Madelon A.; Strehl, Ute; K. Loo, Sandra (1 March 2019). "Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis". European Child & Adolescent Psychiatry. 28 (3): 293–305. doi:10.1007/s00787-018-1121-4. ISSN 1435-165X. PMC 6404655. PMID 29445867.
  26. Enriquez-Geppert, Stefanie; Smit, Diede; Pimenta, Miguel Garcia; Arns, Martijn (28 May 2019). "Neurofeedback as a Treatment Intervention in ADHD: Current Evidence and Practice". Current Psychiatry Reports. 21 (6): 46. doi:10.1007/s11920-019-1021-4. ISSN 1535-1645. PMC 6538574. PMID 31139966.
  27. Dashbozorgi, Zahra; Ghaffari, Amin; Karamali Esmaili, Samaneh; Ashoori, Jamal; Moradi, Ali; Sarvghadi, Pooria (10 September 2021). "Effect of Neurofeedback Training on Aggression and Impulsivity in Children with Attention-Deficit/Hyperactivity Disorder: A Double-Blinded Randomized Controlled Trial". Basic and Clinical Neuroscience. 12 (5): 693–702. doi:10.32598/bcn.2021.2363.1. PMC 8818111. PMID 35173923. S2CID 237880490.
  28. Birbaumer, Niels; Ramos Murguialday, Ander; Weber, Cornelia; Montoya, Pedro (1 January 2009), "Chapter 8 Neurofeedback and Brain–Computer Interface: Clinical Applications", International Review of Neurobiology, Academic Press, 86: 107–117, doi:10.1016/s0074-7742(09)86008-x, PMID 19607994, retrieved 28 April 2023
  29. Coben, Robert; Hammond, D. Corydon; Arns, Martijn (1 March 2019). "19 Channel Z-Score and LORETA Neurofeedback: Does the Evidence Support the Hype?". Applied Psychophysiology and Biofeedback. 44 (1): 1–8. doi:10.1007/s10484-018-9420-6. ISSN 1573-3270. PMC 6373269. PMID 30255461.
  30. Bauer, Robert; Vukelić, Mathias; Gharabaghi, Alireza (1 September 2016). "What is the optimal task difficulty for reinforcement learning of brain self-regulation?". Clinical Neurophysiology. 127 (9): 3033–3041. doi:10.1016/j.clinph.2016.06.016. ISSN 1388-2457. PMID 27472538. S2CID 3686790.
  31. Thibault, Robert T.; Raz, Amir (October 2017). "The psychology of neurofeedback: Clinical intervention even if applied placebo". American Psychologist. 72 (7): 679–688. doi:10.1037/amp0000118. ISSN 1935-990X. PMID 29016171. S2CID 4650115.
  32. Thibault, Robert T.; Lifshitz, Michael; Birbaumer, Niels; Raz, Amir (2015). "Neurofeedback, Self-Regulation, and Brain Imaging: Clinical Science and Fad in the Service of Mental Disorders". Psychotherapy and Psychosomatics. 84 (4): 193–207. doi:10.1159/000371714. ISSN 0033-3190. PMID 26021883. S2CID 17750375.

Further reading
  • Arns M, Sterman MB (2019). Neurofeedback: How it all started. Nijmegen, The Netherlands: Brainclinics Insights. ISBN 9789083001302.
  • Evans JR, Abarbanel A (1999). An introduction to quantitative EEG and Neurofeedback. San Diego: Academic Press.
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