Cited by 18EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyAutism Spectrum Disorder Research

Slow cortical potential BCI classification using sparse variational bayesian logistic regression with automatic relevance determination

Open access
Miladinović, Aleksandar, Ajčević, Miloš, Battaglini, Piero Paolo, Silveri, Giulia, Ciacchi, Gaia, Morra, Giulietta, Jarmolowska, Joanna, Accardo, Agostino·2020·IFMBE Proceedings

Integrity signals

Registry

Crossref · 10.1007/978-3-030-31635-8_225

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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Authors
Miladinović, A., Ajčević, M., Battaglini, P. P., Silveri, G., Ciacchi, G., Morra, G., Jarmolowska, J., & Accardo, A.
Affiliations
University of Trieste (IT)
Corresponding author
Miladinović, Aleksandar
Venue
IFMBE Proceedings
Published
2019-09-25
Pages
1853-1860
ISSN
  • 1433-9277 (ISSN-L)
  • 1680-0737
OpenAlex
W2976368778
Cited by
18
References
2
Citations by year
2025: 4 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 8 · 2019: 1
FWCI
5.17
Type
conference paper
Language
en
Open access
Locations
  • World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany (publishedVersion)
  • ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, other-oa)
Funders
European Social Fund
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
Keywords
P300, slow cortical potentials, event-related potentials, brain-computer interface, automatic relevance determination, variational inference, sparse logistic regression, EEG classification, autism spectrum disorder, joint attention, neurofeedback

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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.

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