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| Research article summary (published 30 May 2003): |
A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces.
Full 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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Author information
Author/s: Sajda, Paul (P); Gerson, Adam (A); Müller, Klaus-Robert (KR); Blankertz, Benjamin (B); Parra, Lucas (L);
Affiliation: Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA. sajda(-atsign-)columbia.edu
Journal and publication information
Publication Type: Comparative Study; Evaluation Studies; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Validation Studies
Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (IEEE Trans Neural Syst Rehabil Eng), published in United States. (Language: eng)
Reference: 2003-Jun; vol 11 (issue 2) : pp 184-5
Dates: Created 2003/08/05; Completed 2003/09/12; Revised 2006/11/15;
PMID: 12899269, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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