Decoding memory processing from electro-corticography in human posteromedial cortex

Recently machine learning models have been applied to neuroimaging data, which allow predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These pattern recognition based methods present clear benefits over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each feature in the model. Machine learning methods have been applied to a range of data, from MRI to EEG. However, these multivariate techniques have scarcely been applied to electrocorticography (ECoG) data to investigate cognitive neuroscience questions. In this work, we used previously published ECoG data from 8 subjects to show that machine learning techniques can complement univariate techniques and be more sensitive to certain effects.