The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials
Abstract
Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.
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- Authors
- Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., Schroder, M., & Birbaumer, N.
- Affiliations
- Charité – Universitätsmedizin Berlin (DE)
- Dept. of Neurology, Charite Univ. Med., Berlin, Germany
- New York State Department of Health (US)
- Graz University of Technology (AT)
- University of Tübingen (DE)
- University of Trento (IT)
- ORCID
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-06
- Volume
- 51
- Issue
- 6
- Pages
- 1044-1051
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2153912116
- Cited by
- 670
- References
- 27
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 22 · 2023: 29 · 2022: 21 · 2021: 27 · 2020: 31 · 2019: 31 · 2018: 38 · 2017: 33 · 2016: 33 · 2015: 29 · 2014: 26 · 2013: 42 · 2012: 24
- FWCI
- 20.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- publish.UP (University of Potsdam) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (EB 00856, R01EB000856)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030146, HD30146)
- 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, event detection, event discrimination, signal classification
- MeSH terms
- Adult, Algorithms, Amyotrophic Lateral Sclerosis, Artificial Intelligence, Brain, Cognition, Electroencephalography, Evoked Potentials, Humans, Sensitivity and Specificity, User-Computer Interface, Reproducibility of Results, Databases, Factual
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