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Brain Sciences
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Advances in Neurofeedback Research
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Advances in Neurofeedback Research

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (25 January 2026) | Viewed by 2754

Special Issue Editor

Prof. Dr. Eddy J. Davelaar

E-Mail Website
Guest Editor
School of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
Interests: memory; cognitive aging; dementia; neurofeedback; computational modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neurofeedback, also known as brain biofeedback, has a long history dating back to the beginnings of cognitive psychology. As a field, it has seen considerable evolution in the applied clinical domain, where it is referred to as neurotherapy, and in the domain of technological communication systems, where brain–computer interfaces involve neurofeedback and machine learning. Whereas most of the literature on neurofeedback focuses on electroencephalography (EEG) as the source of brain signals, other modalities, such as blood-oxygenation-level-dependent (BOLD) signals, have been used successfully. There are differences in methods and research questions between EEG- and BOLD-based neurofeedback. In addition, theoretical advances are influencing research methods and the neurofeedback applications.   

The aim of this Special Issue is to solicit original research articles as well as review articles that showcase the breadth of depth of cutting-edge research into neurofeedback

Prof. Dr. Eddy J. Davelaar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neurofeedback
  • brain–computer interface
  • neurophenomenology
  • EEG
  • rt-fMRI
  • fNIRS-neurofeedback

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Published Papers (2 papers)

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Research

23 pages, 8524 KB  
Article
The Impact of Visual Feedback Design on Self-Regulation Performance and Learning in a Single-Session rt-fMRI Neurofeedback Study at 3T and 7T
by Sebastian Baecke, Ralf Lützkendorf and Johannes Bernarding
Brain Sci. 2026, 16(2), 166; https://doi.org/10.3390/brainsci16020166 - 30 Jan 2026
Viewed by 595
Abstract
Background: The efficacy of real-time fMRI neurofeedback (NFB) depends critically on how feedback is presented and perceived by the participant. Although various visual feedback designs are used in practice, there is limited evidence on the impact of modality on learning and performance. We [...] Read more.
Background: The efficacy of real-time fMRI neurofeedback (NFB) depends critically on how feedback is presented and perceived by the participant. Although various visual feedback designs are used in practice, there is limited evidence on the impact of modality on learning and performance. We conducted a feasibility study to compare the effectiveness of different feedback modalities, and to evaluate the technical performance of NFB across two scanner field strengths. Methods: In a single-session study, nine healthy adults (6 men, 3 women) voluntarily adapted the activation level of the primary sensorimotor cortex (SMC) to reach three predefined activation levels. We contrasted a continuous, signal-proportional feedback (cFB; a thermometer-style bar) with an affect-based, categorical feedback (aFB; a smiling face). A no-feedback transfer condition (noFB) was included to probe regulation based on internal representations alone. To assess technical feasibility, three participants were scanned at 7T and six at 3T. Results: Participants achieved successful regulation in 44.4% of trials overall (cFB 46.9%, aFB 43.8%, noFB 42.6%). Overall success rates did not differ significantly between modalities and field strengths when averaged across the session; given the small feasibility sample, this null result is inconclusive and does not establish equivalence. Learning effects were modality-specific. Only cFB showed a significant within-session improvement (+14.8 percentage points from RUN1 to RUN2; p = 0.031; d_z = 0.94), whereas aFB and noFB showed no evidence of learning. Exploratory whole-brain contrasts (uncorrected) suggested increased recruitment of ipsilateral motor regions during noFB. The real-time pipeline demonstrated robust technical performance: transfer/reconstruction latency averaged 497.8 ms and workstation processing averaged 296.8 ms (≈795 ms end-to-end), with rare stochastic outliers occurring predominantly during 7T sessions. Conclusions: In this single-session motor rt-fMRI NFB paradigm, continuous signal-proportional feedback supported rapid within-session learning, whereas affect-based categorical cues did not yield comparable learning benefits. Stable low-latency operation was achievable at both 3T and 7T. Larger, balanced studies are warranted to confirm modality-by-learning effects and to better characterize transfer to feedback-free self-regulation. Full article
(This article belongs to the Special Issue Advances in Neurofeedback Research)
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19 pages, 2665 KB  
Article
Entropy and Complexity in QEEG Reveal Visual Processing Signatures in Autism: A Neurofeedback-Oriented and Clinical Differentiation Study
by Aleksandar Tenev, Silvana Markovska-Simoska, Andreas Müller and Igor Mishkovski
Brain Sci. 2025, 15(9), 951; https://doi.org/10.3390/brainsci15090951 - 1 Sep 2025
Cited by 1 | Viewed by 1388
Abstract
(1) Background: Quantitative EEG (QEEG) offers potential for identifying objective neurophysiological biomarkers in psychiatric disorders and guiding neurofeedback interventions. This study examined whether three nonlinear QEEG metrics—Lempel–Ziv Complexity, Tsallis Entropy, and Renyi Entropy—can distinguish children with autism spectrum disorder (ASD) from typically developing [...] Read more.
(1) Background: Quantitative EEG (QEEG) offers potential for identifying objective neurophysiological biomarkers in psychiatric disorders and guiding neurofeedback interventions. This study examined whether three nonlinear QEEG metrics—Lempel–Ziv Complexity, Tsallis Entropy, and Renyi Entropy—can distinguish children with autism spectrum disorder (ASD) from typically developing (TD) peers, and assessed their relevance for neurofeedback targeting. (2) Methods: EEG recordings from 19 scalp channels were analyzed in children with ASD and TD. The three nonlinear metrics were computed for each channel. Group differences were evaluated statistically, while machine learning classifiers assessed discriminative performance. Dimensionality reduction with t-distributed Stochastic Neighbor Embedding (t-SNE) was applied to visualize clustering. (3) Results: All metrics showed significant group differences across multiple channels. Machine learning classifiers achieved >90% accuracy, demonstrating robust discriminative power. t-SNE revealed distinct ASD and TD clustering, with nonlinear separability in specific channels. Visual processing–related channels were prominent contributors to both classifier predictions and t-SNE cluster boundaries. (4) Conclusions: Nonlinear QEEG metrics, particularly from visual processing regions, differentiate ASD from TD with high accuracy and may serve as objective biomarkers for neurofeedback. Combining complexity and entropy measures with machine learning and visualization techniques offers a relevant framework for ASD diagnosis and personalized intervention planning. Full article
(This article belongs to the Special Issue Advances in Neurofeedback Research)
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