The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Neurofeedback Training for BCI Control
Overview
Integrity signals
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Cites
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Verification
Claims the paper makes
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 [46]
In the citing paper
“To begin with, subject learning in online MI BCI is most often hypothesized to occur “by default” in analogy to neurofeedback training [1,46,54,55].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [46]
In the citing paper
“Several indications assert that the learning effects observed here correspond to instrumental learning, as traditionally hypothesized [46].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [46]
In the citing paper
“Hence, we consider that deriving some index of neuroimaging-based separability at the feature level in order to quantify the user’s BCI aptitude (and its evolution over time) is a sine qua non prerequisite for corroborating the existence of subject learning [46].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [46]
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
Self-citationMotor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990
Self-citationBrain–machine interfaces: past, present and future
Lebedev, M. A., & Nicolelis, M. A. L. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.07.004 · Cited by 1,875
The functional states of the thalamus and the associated neuronal interplay.
Steriade, M., & Llinas, R. R. (1988) · Physiological Reviews · doi:10.1152/physrev.1988.68.3.649 · Cited by 1,746
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
An EEG-based brain-computer interface for cursor control
Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90040-b · Cited by 1,117
Neural mechanisms underlying brain waves: from neural membranes to networks
Lopes da Silva, F. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90044-5 · Cited by 1,112
Motor imagery activates primary sensorimotor area in humans
Pfurtscheller, G., & Neuper, C. (1997) · Neuroscience Letters · doi:10.1016/s0304-3940(97)00889-6 · Cited by 1,052
Self-citationActivation of human primary motor cortex during action observation: A neuromagnetic study
Hari, R., Forss, N., Avikainen, S., Kirveskari, E., Salenius, S., & Rizzolatti, G. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.15061 · Cited by 983
Blankertz, B., Dornhege, G., Krauledat, M., Müller, K., & Curio, G. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.01.051 · Cited by 941
Research works printed but not linked: 3
- S. Othmer, S.F. Othmer, and D.A. Kaiser, EEG biofeedback: An emerging model for ist global efficacy. In J. Evans and A. Abarbanel (Eds.), Quantitative EEG and neurofeedback, Academic, pp. 243–310, (1999).
- J.V. Hardt, The ups and downs of learning alpha feedback. Biofeedback Res Soc, 6, 118, (1975).
- R. Ron-Angevin, A. Diaz-Estrella, and A. Reyes-Lecuona, Development of a brain-computer interface based on virtual reality to improve training techniques. Cyberpsychol Behav, 8, 353–354, (2005).
Cited by
Sorted by citations
Items 101–102 of 102
Khatri, U. U., Suresh, T., Tatz, J. R., & Hussain, S. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.07.743250
Bouallagui, H., Chniter, H., Ghaffari, F., & Romain, O. (2026) · Intelligenza Artificiale · doi:10.1177/17248035261480407
Metadata
- Authors
- Neuper, C., & Pfurtscheller, G.
- Affiliations
- University of Graz (AT)
- Corresponding author
- Neuper, Christa
- ORCID
- Neuper, Christa
- Venue
- The Frontiers Collection
- Publisher
- Springer Berlin Heidelberg
- Published
- 2009
- Pages
- 65-78
- ISSN
- 1612-3018 (ISSN-L)
- OpenAlex
- W1891739631
- Cited by
- 102
- References
- 68
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 4 · 2023: 12 · 2022: 3 · 2021: 11 · 2020: 5 · 2019: 7 · 2018: 12 · 2017: 6 · 2016: 8 · 2015: 7 · 2014: 7 · 2013: 6 · 2012: 4
- FWCI
- 3.20
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- The frontiers collection (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- neurofeedback, brain-computer interface, neural signal classification, EEG, electrocorticography, neural decoding, pattern recognition
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.
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