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.
Original language | English |
---|---|
Pages (from-to) | 184-185 |
Number of pages | 2 |
Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Volume | 11 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2003 Jun |
Bibliographical note
Funding Information:Manuscript received August 16, 2002; revised April 29, 2003. This work was supported by the Defense Advanced Research Projects Agency (DARPA). P. Sajda and A. Gerson are with the Department of Biomedical Engineering, Columbia University, New York, NY 10027 USA (e-mail:[email protected]; [email protected]). K.-R. Müller and B. Blankertz are with Fraunhofer FIRST, D-12489 Berlin, Germany (e-mail: [email protected], [email protected]). L. Parra is with Sarnoff Corporation, Princeton, NJ 08540 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TNSRE.2003.814453
Keywords
- Brain computer interface (BCI)
- Data analysis competition
- Electroencephalography (EEG)
- Machine learning
ASJC Scopus subject areas
- Internal Medicine
- General Neuroscience
- Biomedical Engineering