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Cited by 1,288EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesGaze Tracking and Assistive Technology

Review of the BCI Competition IV

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Tangermann, Michael, Müller, Klaus-Robert, Aertsen, Ad, Birbaumer, Niels, Braun, Christoph, Brunner, Clemens, Leeb, Robert, Mehring, Carsten +10 more·2012·Frontiers in Neuroscience
DOI 10.3389/fnins.2012.00055PMC PMC3396284
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Abstract

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.

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  • The BBCI datasets were limited in terms of subjects and sessions-per-subject, thus constraining the development of methods highly dependent on multi-session data.

    “Nevertheless, those datasets were limited in terms of subjects and sessions-per-subject, thus constraining the development of methods highly dependent on multi-session data.”

    From BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces (2020) · cites this paper as Tangermann et al., 2012

    Data Set 2b’s Materials and subjects section reports nine subjects with five sessions each, and its Discussion identifies session-to-session transfer as a challenge. The paper does not say that the number of subjects or sessions constrained the development of methods highly dependent on multi-session data. Page numbers are unavailable in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • FBCSP won other similar EEG decoding competitions.

    “FBCSP also won other similar EEG decoding competitions [Tangermann et al., 2012].”

    From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Tangermann et al., 2012

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Cited by

Sorted by citations

Authors
Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., Nolte, G., Pfurtscheller, G., Preissl, H., Schalk, G., Schlögl, A., Vidaurre, C., Waldert, S., & Blankertz, B.
Affiliations
  • Technische Universität Berlin (DE)
  • University of Freiburg (DE)
  • IRCCS San Camillo Hospital (IT)
  • University of Trento (IT)
  • University of California San Diego (US)
  • École Polytechnique Fédérale de Lausanne (CH)
  • University of Washington (US)
  • Fraunhofer-Gesellschaft (DE)
  • Graz University of Technology (AT)
  • University of Arkansas for Medical Sciences (US)
  • New York State Department of Health (US)
  • Institute of Science and Technology Austria (AT)
Corresponding author
Tangermann, Michael
ORCID
Venue
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Published
2012
Volume
6
Pages
55-55
ISSN
  • 1662-453X (ISSN-L)
  • 1662-4548
DOI
10.3389/fnins.2012.00055
PMC
PMC3396284
OpenAlex
W2119163516
Cited by
1,288
References
4
Citations by year
2026: 159 · 2025: 180 · 2024: 172 · 2023: 168 · 2022: 116 · 2021: 109 · 2020: 82 · 2019: 70 · 2018: 57 · 2017: 40 · 2016: 39 · 2015: 31 · 2014: 36 · 2013: 22 · 2012: 6
FWCI
15.57
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Digital Commons@Becker (Washington University School of Medicine) (submittedVersion)
  • DepositOnce (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
  • Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
  • Technische Universität Berlin – Universitätsbibliothek (cc-by)
Funders
  • Heidelberger Akademie der Wissenschaften
  • European Commission (216886)
  • National Research Foundation
  • Deutsche Forschungsgemeinschaft (01GQ0850, MU 987/3-2)
  • Bundesministerium für Bildung und Forschung (01GQ0762, 01GQ0761, 01GQ0850, 01GQ0420, 01GQ0830, 01IB001A)
  • National Research Foundation of Korea (R31‐10008, 01GQ0850)
  • Studienstiftung des Deutschen Volkes
  • National Institutes of Health
  • National Institute of Biomedical Imaging and Bioengineering (R01EB006356, R01EB000856)
  • Army Research Office (W911NF-07-1-0415, W911NF-08-1, W911NF, W911NF-08-1-0216)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords
brain-computer interface, BCI Competition IV, asynchronous data, synthetic data, electrocorticography

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