  Electroencephalography (EEG) is a useful way to implicitly monitor the users
perceptual state during multimedia consumption. One of the primary challenges
for the practical use of EEG-based monitoring is to achieve a satisfactory
level of accuracy in EEG classification. Connectivity between different brain
regions is an important property for the classification of EEG. However, how to
define the connectivity structure for a given task is still an open problem,
because there is no ground truth about how the connectivity structure should be
in order to maximize the classification performance. In this paper, we propose
an end-to-end neural network model for EEG-based emotional video
classification, which can extract an appropriate multi-layer graph structure
and signal features directly from a set of raw EEG signals and perform
classification using them. Experimental results demonstrate that our method
yields improved performance in comparison to the existing approaches where
manually defined connectivity structures and signal features are used.
Furthermore, we show that the graph structure extraction process is reliable in
terms of consistency, and the learned graph structures make much sense in the
viewpoint of emotional perception occurring in the brain.
