A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces.
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.
Sajda
Paul
P
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA. sajda@columbia.edu
Gerson
Adam
A
Müller
Klaus-Robert
KR
Blankertz
Benjamin
B
Parra
Lucas
L
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D016208:Databases, Factual; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology; D005246:Feedback; D005385:Fingers / Q000502:physiology; D006801:Humans; D009068:Movement / Q000502:physiology; D010349:Patient Compliance; D010775:Photic Stimulation / Q000379:methods; D013850:Thinking / Q000502:physiology