Prediction of Finger Flexion IV Brain-Computer Interface Data Competition

The goal of this dataset is to predict the flexion of individual fingers from signals recorded from the surface of the brain (electrocorticography (ECoG)). This data set contains brain signals from three subjects, as well as the time courses of the flexion of each of five fingers. The task in this competition is to use the provided flexion information in order to predict finger flexion for a provided test set. The performance of the classifier will be evaluated by calculating the average correlation coefficient r between actual and predicted finger flexion.

ECoG data during individual flexions of the five fingers; movements acquired with a data glove. [48 - 64 ECoG channels (0.15-200Hz), 1000Hz sampling rate, 3 subjects]