A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces

Paul Sajda, Adam Gerson, Klaus Robert Müller, Benjamin Blankertz, Lucas Parra

Research output: Contribution to journalArticlepeer-review

105 Citations (Scopus)


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 languageEnglish
Pages (from-to)184-185
Number of pages2
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
Publication statusPublished - 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:sajda@columbia.edu; adg71@columbia.edu). K.-R. Müller and B. Blankertz are with Fraunhofer FIRST, D-12489 Berlin, Germany (e-mail: klaus@first.fraunhofer.de, blanker@first.fraunhofer.de). L. Parra is with Sarnoff Corporation, Princeton, NJ 08540 USA (e-mail: lparra@sarnoff.com). Digital Object Identifier 10.1109/TNSRE.2003.814453


  • Brain computer interface (BCI)
  • Data analysis competition
  • Electroencephalography (EEG)
  • Machine learning

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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