计算机科学
脑电图
人工智能
卷积神经网络
图形
模式识别(心理学)
特征提取
自然语言处理
语音识别
理论计算机科学
心理学
神经科学
作者
Kun Yang,Zhenning Yao,Keze Zhang,Jing Xu,Li Zhu,Shichao Cheng,Jianhai Zhang
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2024-05-22
卷期号:28 (8): 4588-4598
标识
DOI:10.1109/jbhi.2024.3404146
摘要
Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and edges are invalid information or even interference information for the current task. It is necessary to reduce the network dimension and extract the core network. To address the problem of extracting and utilizing the core network, a core network extraction model (CWGCN) based on channel weighting and graph convolutional network and a graph convolutional network model (CCSR-GCN) based on channel convolution and style-based recalibration for emotion recognition have been proposed. The CWGCN model automatically extracts the core network and the channel importance parameter in a data-driven manner. The CCSR-GCN model innovatively uses the output information of the CWGCN model to identify the emotion state. The experimental results on SEED show that: (1) the core network extraction can help improve the performance of the GCN model; (2) the models of CWGCN and CCSR-GCN achieve better results than the currently popular methods. The idea and its implementation in this paper provide a novel and successful perspective for the application of GCN in brain network analysis of other specific tasks. The code is available at https://github.com/ykhdu/CWGCN-CCSR-GCN .
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