School of Integrated Technology, Yonsei University, Republic of Korea.
Department of Statistics, University of California, Davis, USA.
Neural Netw. 2020 Dec;132:96-107. doi: 10.1016/j.neunet.2020.08.009. Epub 2020 Aug 19.
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（CNNs）被广泛用于通过脑电图（EEG）信号识别用户的状态。在以前的研究中，EEG 信号通常以高维原始数据的形式输入到 CNN 中。然而，这种方法使得利用大脑连接信息变得困难，而这些信息对于描述功能大脑网络和估计用户的感知状态是非常有效的。我们引入了一种新的分类系统，该系统利用大脑连接和 CNN 来验证其有效性，通过使用三种不同类型的连接度量对情感视频进行分类。此外，还提出了两种基于数据驱动的方法来构建连接矩阵，以最大限度地提高分类性能。进一步的分析表明，与目标视频的情感属性相关的大脑连接的集中程度与分类性能相关。
