Predictors of neurofeedback training outcome: A systematic review
Open accessAbstract
Neurofeedback (NF), a training tool aimed at enhancing neural self-regulation, has been suggested as a complementary treatment option for neuropsychiatric disorders. Despite its potential as a neurobiological intervention directly targeting neural alterations underlying clinical symptoms, the efficacy of NF for the treatment of mental disorders has been questioned recently by negative findings obtained in randomized controlled trials (e.g., Cortese et al., 2016). A possible reason for insufficient group effects of NF trainings vs. placebo could be related to the high rate of participants who fail to self-regulate brain activity by NF ("non-learners"). Another reason could be the application of standardized NF protocols not adjusted to individual differences in pathophysiology. Against this background, we have summarized information on factors determining training and treatment success to provide a basis for the development of individualized training protocols and/or clinical indications. The present systematic review included 25 reports investigating predictors for the outcome of NF trainings in healthy individuals as well as patients affected by mental disorders or epilepsy. We selected these studies based on searches in EBSCOhost using combinations of the keywords "neurofeedback" and "predictor/predictors". As "NF training" we defined all NF applications with at least two sessions. The best available evidence exists for neurophysiological baseline parameters. Among them, the target parameters of the respective training seem to be of particular importance. However, particularities of the different experimental designs and outcome criteria restrict the interpretability of some of the information we extracted. Therefore, further research is needed to gain more profound knowledge about predictors of NF outcome.
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Assessments of differential EEG-learning are complicated by a lack of standardized criteria for determining learning.
“That said, assessments of differential EEG-learning are complicated by a lack of standardized criteria for the determination of learning (or a lack thereof) [129].”
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“Predictors for learning ability Diaz Hernandez et al. (2018) reported a negative correlation between motive satisfaction in terms of satisfactory realization of individual goals as assessed by a life-satisfaction questionnaire and NF training performance in a paradigm of EEG-based microstates.”
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Exploring Background Noise During Learning: A Neurofeedback Study
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- Authors
- Weber, L. A., Ethofer, T., & Ehlis, A.-C.
- Affiliations
- Universitätsklinikum Tübingen (DE)
- Corresponding author
- Weber, Lydia Anna
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 27
- Pages
- 102301
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC7327249
- OpenAlex
- W3031440461
- Cited by
- 81
- References
- 56
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 14 · 2023: 15 · 2022: 16 · 2021: 9 · 2020: 3
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Bundesministerium für Bildung und Forschung
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Keywords
- neurofeedback, predictors of treatment outcome, neuropsychiatric disorders, epilepsy, placebo effect
- MeSH terms
- Mental Disorders, Brain, Electroencephalography, Humans, Learning, Neurofeedback, Brain-Computer Interfaces
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