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.
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The Generic Object Decoding (GOD) Dataset is a specialized resource developed for fMRI-based decoding. It aggregates fMRI data gathered through the presentation of images from 200 representative object categories, originating from the 2011 fall release of ImageNet. The training session incorporated 1,200 images (8 per category from 150 distinct object categories). In contrast, the test session included 50 images (one from each of the 50 object categories). It is noteworthy that the categories in the test session were unique from those in the training session and were introduced in a randomized sequence across runs. On five subjects the fMRI scanning was conducted.
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