  Decoding cognitive states from functional magnetic resonance imaging is
central to understanding the functional organization of the brain.
Within-subject decoding avoids between-subject correspondence problems but
requires large sample sizes to make accurate predictions; obtaining such large
sample sizes is both challenging and expensive. Here, we investigate an
ensemble approach to decoding that combines the classifiers trained on data
from other subjects to decode cognitive states in a new subject. We compare it
with the conventional decoding approach on five different datasets and
cognitive tasks. We find that it outperforms the conventional approach by up to
20% in accuracy, especially for datasets with limited per-subject data. The
ensemble approach is particularly advantageous when the classifier is trained
in voxel space. Furthermore, a Multi-layer Perceptron turns out to be a good
default choice as an ensemble method. These results show that the pre-training
strategy reduces the need for large per-subject data.
