A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces.
Abstract
We present three datasets that were used to conduct an open competition for evaluating the performance of various machine-learning algorithms used in brain-computer interfaces. The datasets were collected for tasks that included: 1) detecting explicit left/right (L/R) button press; 2) predicting imagined L/R button press; and 3) vertical cursor control. A total of ten entries were submitted to the competition, with winning results reported for two of the three datasets.
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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 Sajda et al., 2003
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Simões, M., Borra, D., Santamaría-Vázquez, E., GBT-UPM, Bittencourt-Villalpando, M., Krzemiński, D., Miladinović, A., Neural_Engineering_Group, Schmid, T., Zhao, H., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.568104 · Cited by 72 · see citing works
Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016
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- Authors
- Sajda, P., Gerson, A., Muller, K.-R., Blankertz, B., & Parra, L.
- Affiliations
- Columbia University (US)
- Fraunhofer-Gesellschaft (DE)
- ORCID
- Blankertz, B.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2003-06
- Volume
- 11
- Issue
- 2
- Pages
- 184-185
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2099579406
- Cited by
- 139
- References
- 0
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 10 · 2023: 8 · 2022: 6 · 2021: 6 · 2020: 7 · 2019: 5 · 2018: 10 · 2017: 2 · 2016: 8 · 2015: 7 · 2014: 3 · 2013: 3 · 2012: 5
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- brain-computer interface, EEG, motor imagery
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Electroencephalography, Evoked Potentials, Visual, Feedback, Fingers, Humans, Movement, Patient Compliance, Photic Stimulation, Thinking, Databases, Factual
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