期刊
NEURAL NETWORKS
卷 132, 期 -, 页码 96-107出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2020.08.009
关键词
Electroencephalography (EEG); Convolutional neural network (CNN); Brain connectivity; Emotion
资金
- MSIT (Ministry of Science and ICT), Korea, under the ICT Consilience Creative Program'' [IITP-2019-2017-0-01015]
- US National Science Foundation [DMS-19-14917]
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.
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