  Neuroimaging techniques have shown to be useful when studying the brain's
activity. This paper uses Magnetoencephalography (MEG) data, provided by the
Human Connectome Project (HCP), in combination with various deep artificial
neural network models to perform brain decoding. More specifically, here we
investigate to which extent can we infer the task performed by a subject based
on its MEG data. Three models based on compact convolution, combined
convolutional and long short-term architecture as well as a model based on
multi-view learning that aims at fusing the outputs of the two stream networks
are proposed and examined. These models exploit the spatio-temporal MEG data
for learning new representations that are used to decode the relevant tasks
across subjects. In order to realize the most relevant features of the input
signals, two attention mechanisms, i.e. self and global attention, are
incorporated in all the models. The experimental results of cross subject
multi-class classification on the studied MEG dataset show that the inclusion
of attention improves the generalization of the models across subjects.
