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

doi:10.1109/tnsre.2003.814453

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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

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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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Algorithms Artificial Intelligence Brain Databases, Factual Electroencephalography Evoked Potentials, Visual Feedback Fingers Humans Movement Patient Compliance Photic Stimulation Thinking Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Validation Study

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Paul Sajda, Adam Gerson, Klaus-Robert Müller, Benjamin Blankertz, Lucas Parra. 2003. A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces.. https://doi.org/10.1109/tnsre.2003.814453

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