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
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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
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Self-citationSuppression of seizures in an epileptic following sensorimotor EEG feedback training
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Enhancement of Left-Right Sensorimotor EEG Differences During Feedback-Regulated Motor Imagery
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Self-citationA brain-computer interface with vibrotactile biofeedback for haptic information
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Self-citationValidating the efficacy of neurofeedback for optimising performance
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Vibrotactile Feedback for Brain-Computer Interface Operation
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Self-citationAn Auditory Brain-Computer Interface Based on the Self-Regulation of Slow Cortical Potentials
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Automated feature selection with a distinction sensitive learning vector quantizer
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Viewing Moving Objects in Virtual Reality Can Change the Dynamics of Sensorimotor EEG Rhythms
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Self-citation
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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
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