  Convolutional neural networks (CNNs) are widely used to recognize the user's
state through electroencephalography (EEG) signals. In the previous studies,
the EEG signals are usually fed into the CNNs in the form of high-dimensional
raw data. However, this approach makes it difficult to exploit the brain
connectivity information that can be effective in describing the functional
brain network and estimating the perceptual state of the user. We introduce a
new classification system that utilizes brain connectivity with a CNN and
validate its effectiveness via the emotional video classification by using
three different types of connectivity measures. Furthermore, two data-driven
methods to construct the connectivity matrix are proposed to maximize
classification performance. Further analysis reveals that the level of
concentration of the brain connectivity related to the emotional property of
the target video is correlated with classification performance.
