The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.
The Do's and Don'ts of Neurofeedback Training: A Review of the Controlled Studies Using Healthy Adults
Open accessAbstract
The goal of EEG neurofeedback (EEG-NFB) training is to induce changes in the power of targeted EEG bands to produce beneficial changes in cognitive or motor function. The effectiveness of different EEG-NFB protocols can be measured using two dependent variables: (1) changes in EEG activity and (2) behavioral changes of a targeted function (for therapeutic applications the desired changes should be long-lasting). To firmly establish a causal link between these variables and the selected protocol, similar changes should not be observed when appropriate control paradigms are used. The main objective of this review is to evaluate the evidence, reported in the scientific literature, which supports the validity of various EEG-NFB protocols. Our primary concern is to highlight the role that uncontrolled nonspecific factors can play in the results generated from EEG-NFB studies. Nonspecific factors are often ignored in EEG-NFB designs or the data are not presented, which means conclusions should be interpreted cautiously. As an outcome of this review we present a do's and don'ts list, which can be used to develop future EEG-NFB methodologies, based on the small set of experiments in which the proper control groups have excluded non-EEG-NFB related effects. We found two features which positively correlated with the expected changes in power of the trained EEG band(s): (1) protocols which focused on training a smaller number of frequency bands and (2) a bigger number of electrodes used for neurofeedback training. However, we did not find evidence in support of the positive relationship between power changes of a trained frequency band(s) and specific behavioral effects.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, this extrapolation is by no means straightforward, as neurofeedback typically exerts lesser demands, requiring control over predefined brain signals by direct observation [56], while SMR BCIs are complex pattern-recognition systems feeding back transformations of multivariate brain activity [46].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [56]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lakens, D. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00863 · Cited by 10,303
Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices
Buschman, T. J., & Miller, E. K. (2007) · Science · doi:10.1126/science.1138071 · Cited by 2,380
Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data
Hauke, J., & Kossowski, T. (2011) · Quaestiones Geographicae · doi:10.2478/v10117-011-0021-1 · Cited by 2,200
Recommended effect size statistics for repeated measures designs
Bakeman, R. A. (2005) · Behavior Research Methods · doi:10.3758/bf03192707 · Cited by 1,813
Video game training enhances cognitive control in older adults
Anguera, J. A., Boccanfuso, J., Rintoul, J. L., Al-Hashimi, O., Faraji, F., Janowich, J., Kong, E., Larraburo, Y., Rolle, C., Johnston, E., et al. (2013) · Nature · doi:10.1038/nature12486 · Cited by 1,666
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×
“A similar observation was reported by Arns et al. (2009) in conclusion of their meta-analysis: while EEG-NFB therapy provided, on average, positive therapeutic effects for children with ADHD, there was no difference in the effect size for different EEGNFB protocols (SCP [0.5–2Hz], SMR…”
BackgroundMethodResultInfluentialGruzelier, J. H. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.09.015 · Cited by 596
Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance
Zoefel, B., Huster, R. J., & Herrmann, C. S. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.078 · Cited by 522
The effect of training distinct neurofeedback protocols on aspects of cognitive performance
Vernon, D. J., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., & Gruzelier, J. H. (2003) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(02)00091-0 · Cited by 491
Egner, T., & Gruzelier, J. H. (2003) · Clinical Neurophysiology · doi:10.1016/s1388-2457(03)00353-5 · Cited by 473
Flagged first, then by citations
1 of the 74 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Fender, 1987, Source localization of brain electrical activity, 355
Cited by
Sorted by citations
Items 101–115 of 115
Effects of upper alpha neurofeedback training on event related potential P300
Haba, T., Cui, G., & Touyama, H. (2023) · International Symposium on Affective Science and Engineering · doi:10.5057/isase.2023-c000024
Collura, T. F., Keizer, A. W., Pérez-Elvira, R., Warner, S., & Feiner, T. F. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-89827-0.00027-9
Spinal column and midbrain integration for long duration space missions
Richardson, T. (2023) · F1000Research · doi:10.12688/f1000research.129719.1
Velásquez-Pérez, J. L., & Rizo-Arévalo, A. (2023) · Revista de Psicología Universidad de Antioquia · doi:10.17533/udea.rp.e347277
Electroencephalogram (EEG) Signal Processing
Sharma, R. K., Mishra, S. K., & Fatima, S.-E. (2023) · doi:10.53555/sfs.v10i2.2737
Puyvelde, M. V., & Pattyn, N. (2024) · Routledge eBooks · doi:10.4324/9781003378969-11
Zhang, B., Chen, T., & Yan, L. (2024) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai61503.2024.10607542
Zhang, M., Wei, C., Ding, C., Zhao, J., Sun, J., Lin, L., Chen, Y., Li, M., Li, X., Cheng, M., et al. (2026) · Behavioral and Brain Functions · doi:10.1186/s12993-026-00351-z
Grokhotova, А. V., Nagornova, Z. V., & Shemyakina, N. V. (2025) · Human Physiology · doi:10.1134/s0362119725700549
Successful single-session neural self-regulation through neurofeedback varies between featuresPreprint
Syrjänen, E., Silva, J., & Ästrand, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.07.698228
Glazebrook, A. J., Shakespeare‐Finch, J., Andrew, B., & van der Meer, J. N. (2026) · Journal of Loss and Trauma · doi:10.1080/15325024.2025.2585079
Ethical risks and considerations of brain-controlled and neuromodulation technologies
Huang, K., Yang, H., Zhu, S., Chen, Y., Li, T., Zhao, L., Gong, A., Nan, W., Xu, J., & Fu, Y. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10445-z
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
Successful Single‐Session Neural Self‐Regulation Through Neurofeedback Varies Between Features
Syrjänen, E., Silva, J., & Ästrand, E. (2026) · Human Brain Mapping · doi:10.1002/hbm.70611
Metadata
- Authors
- Rogala, J., Jurewicz, K., Paluch, K., Kublik, E., Cetnarski, R., & Wróbel, A.
- Affiliations
- Instytut Biologii Doświadczalnej im. Marcelego Nenckiego (PL)
- Corresponding author
- Rogala, Jacek
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2016-06-17
- Volume
- 10
- Pages
- 301-301
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC4911408
- OpenAlex
- W2426762202
- Cited by
- 115
- References
- 74
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 6 · 2023: 15 · 2022: 12 · 2021: 14 · 2020: 14 · 2019: 13 · 2018: 10 · 2017: 12
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Narodowe Centrum Nauki (2012/07/B/NZ7/04383)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Keywords
- neurofeedback, EEG band power, non-specific effects, control groups, electrode number, behavioral effects, healthy adults, causal inference
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
