  Since machine learning models have been applied to neuroimaging data,
researchers have drawn conclusions from the derived weight maps. In particular,
weight maps of classifiers between two conditions are often described as a
proxy for the underlying signal differences between the conditions. Recent
studies have however suggested that such weight maps could not reliably recover
the source of the neural signals and even led to false positives (FP). In this
work, we used semi-simulated data from ElectroCorticoGraphy (ECoG) to
investigate how the signal-to-noise ratio and sparsity of the neural signal
affect the similarity between signal and weights. We show that not all cases
produce FP and that it is unlikely for FP features to have a high weight in
most cases.
