The DOI matches its Crossref record; 112 of the 113 references it cites were found in a registry or an index.
Electroencephalographic neurofeedback: Level of evidence in mental and brain disorders and suggestions for good clinical practice
Abstract
The technique of electroencephalographic neurofeedback (EEG NF) emerged in the 1970s and is a technique that measures a subject's EEG signal, processes it in real time, extracts a parameter of interest and presents this information in visual or auditory form. The goal is to effectuate a behavioural modification by modulating brain activity. The EEG NF opens new therapeutic possibilities in the fields of psychiatry and neurology. However, the development of EEG NF in clinical practice requires (i) a good level of evidence of therapeutic efficacy of this technique, (ii) a good practice guide for this technique. Firstly, this article investigates selected trials with the following criteria: study design with controlled, randomized, and open or blind protocol, primary endpoint related to the mental and brain disorders treated and assessed with standardized measurement tools, identifiable EEG neurophysiological targets, underpinned by pathophysiological relevance. Trials were found for: epilepsies, migraine, stroke, chronic insomnia, attentional-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, major depressive disorder, anxiety disorders, addictive disorders, psychotic disorders. Secondly, this article investigates the principles of neurofeedback therapy in line with learning theory. Different underlying therapeutic models are presented didactically between two continua: a continuum between implicit and explicit learning and a continuum between the biomedical model (centred on "the disease") and integrative biopsychosocial model of health (centred on "the illness"). The main relevant learning model is to link neurofeedback therapy with the field of cognitive remediation techniques. The methodological specificity of neurofeedback is to be guided by biologically relevant neurophysiological parameters. Guidelines for good clinical practice of EEG NF concerning technical issues of electrophysiology and of learning are suggested. These require validation by institutional structures for the clinical practice of EEG NF.
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“Attention and hyperactivity disorders, depression disorders, stroke, epilepsy, migraine and chronic insomnia are some of the conditions for which NFB is most commonly used.4”
In the citing paper
“Notably, no explicit strategies or instructions were given as, with few exceptions [204], implicit strategies have been shown to produce better outcomes [205–210].”
In the citing paper
“Manual, rather than automated, thresholding was chosen as it has been reported to lead to better EEG-learning [125, 173, 206, 208].”
Citations
Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Are theories of learning necessary?
Skinner, B. F. (1950) · Psychological Review · doi:10.1037/h0054367 · Cited by 1,808
Silberstein, S. D. (2000) · Neurology · doi:10.1212/wnl.55.6.754 · Cited by 1,363
Sonuga-Barke, E. J. S., Brandeis, D., Cortese, S., Daley, D., Ferrin, M., Holtmann, M., Stevenson, J., Danckaerts, M., van der Oord, S., Döpfner, M., et al. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12070991 · Cited by 1,167
Brain–computer interfaces in neurological rehabilitation
Daly, J. J., & Wolpaw, J. R. (2008) · The Lancet Neurology · doi:10.1016/s1474-4422(08)70223-0 · Cited by 1,149
Depression: Perspectives from Affective Neuroscience
Davidson, R. J., Pizzagalli, D., Nitschke, J. B., & Putnam, K. (2002) · Annual Review of Psychology · doi:10.1146/annurev.psych.53.100901.135148 · Cited by 1,138
Vigilance, alertness, or sustained attention: physiological basis and measurement
Oken, B. S., Salinsky, M. C., & Elsas, S. M. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.01.017 · Cited by 887
Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009) · Clinical EEG and Neuroscience · doi:10.1177/155005940904000311 · Cited by 794 · cited 5×
“[9] Arns M, de Ridder S, Strehl U, Breteler M, Coenen A.”
InfluentialDavidson, R. J. (1998) · Psychophysiology · doi:10.1017/s0048577298000134 · Cited by 646 · cited 3×
“[28] Davidson RJ.”
A Decade of EEG Theta/Beta Ratio Research in ADHD: A Meta-Analysis
Arns, M., Conners, C. K., & Kraemer, H. C. (2013) · Journal of Attention Disorders · doi:10.1177/1087054712460087 · Cited by 608
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1 of the 113 references couldn’t be matched to a known paper.
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- Peniston, 1991, Alpha-theta brainwave neurofeedback therapy for Vietnam veterans with combat-related post-traumatic stress disorder, Med Psychother, 4, 47
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Items 101–111 of 111
EEG-neurofeedback as a tool to improve athletic performance
Jeunet, C., Izac, M., Ségas, E., Guerrini, C., N’Kaoua, B., & Pillette, L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24824-5.00014-4
Integrative approaches to specialized populations
Reddy, K. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-43908-7.00011-2
Chowdhury, N. S., Hesam‐Shariati, N., Quidé, Y., Zahara, P., Herbert, R., Restrepo, S., Chen, K. Y., Mcyntire, A., John, T. N., Middleton, J. W., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9142924/v1
Gaume, A. (2016) · theses.fr (ABES) · doi:10.70675/53cc1893z83e8z4d77za68ez96adfd9528ee · Cited by 2
Non-specific increase in alpha power during a neurofeedback session targeting its downregulation
Maaz, J., Dia, A., Waroquier, L., Paban, V., & Rey, A. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1258
Carmichael, D. W., Meagher, R., Sadilova, A., Warbrick, T., & Jaeger, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-20450-9_33
Stanković, I., Jeličić, L., Đorđević, J., Ilic, N. V., Sovilj, M., Maksimović, S., Marisavljević, M., & Subotić, M. (2026) · Brain Sciences · doi:10.3390/brainsci16080843
Barbini, C. M., Merino, D., Manera, V., Fabre, R., Quaderi, A., Askenazy-Gittard, F., & Thümmler, S. (2026) · L Encéphale · doi:10.1016/j.encep.2026.06.007
Metadata
- Authors
- Micoulaud-Franchi, J.-A., McGonigal, A., Lopez, R., Daudet, C., Kotwas, I., & Bartolomei, F.
- Affiliations
- Inserm (FR)
- Hôpital Gui de Chauliac (FR)
- Université de Bordeaux (FR)
- Aix-Marseille Université (FR)
- Venue
- Neurophysiologie Clinique/Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 45
- Issue
- 6
- Pages
- 423-433
- ISSN
- 0987-7053 (ISSN-L)
- OpenAlex
- W2194336409
- Cited by
- 111
- References
- 113
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 10 · 2023: 6 · 2022: 13 · 2021: 16 · 2020: 14 · 2019: 13 · 2018: 9 · 2017: 8 · 2016: 8
- FWCI
- 0.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurophysiologie Clinique (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- neurofeedback, level of evidence, mental illness, brain disorders, epilepsy, migraine, stroke, chronic insomnia, attention deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, anxiety disorders, addiction, psychosis, explicit and implicit learning, biopsychosocial model, cognitive remediation, learning theory, treatment efficacy
- MeSH terms
- Mental Disorders, Brain Diseases, Electroencephalography, Humans, Evidence-Based Medicine, Neurofeedback
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