Emotional EEG classification using connectivity features and convolutional neural networks
Emotional EEG classification using connectivity features and convolutional neural networks
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DOI：
10.1016/j.neunet.2020.08.009
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发表时间：
2020-12-01
期刊：
影响因子：
7.8
通讯作者：
Lee, Jong-Seok
中科院分区：
文献类型：
--
作者：
Moon, Seong-Eun;Chen, Chun-Jui;Lee, Jong-Seok
卷积神经网络（CNN）被广泛用于通过脑电图（EEG）信号来识别用户的状态。在以往的研究中，脑电信号通常以高维原始数据的形式输入到CNN中。然而，这种方法使得难以利用在描述功能性大脑网络和估计用户的感知状态中可以有效的大脑连接信息。我们引入了一种新的分类系统，该系统利用了CNN的大脑连接，并通过使用三种不同类型的连接性度量来验证其有效性。此外，提出了两种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。(c)2020爱思唯尔有限公司保留所有权利。
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. (c) 2020 Elsevier Ltd. All rights reserved.