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.

卷积神经网络（CNN）被广泛用于通过脑电图（EEG）信号识别用户的状态。在先前的研究中，脑电信号通常以高维原始数据的形式馈入CNN。但是，这种方法使利用大脑连接信息变得困难，该信息可以有效地描述功能性大脑网络并估计用户的感知状态。我们引入了一种新的分类系统，该系统利用了CNN与大脑的连通性，并通过使用三种不同类型的连通性度量通过情感视频分类来验证其有效性。此外，提出了两种数据驱动的方法来构造连通性矩阵以最大化分类性能。