计算机工程

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基于多通道脑电信号的因果网络情绪识别

  

  • 发布日期:2021-01-14

Causal network emotion recognition based on multi-channel EEG signal

  • Published:2021-01-14

摘要: 情绪是由大脑内多个通道共同作用产生的,但在计算任意两个通道之间的因果关系时往往忽略其它通道的影响。因 此,引入条件格兰杰提出一种基于多通道脑电信号的因果网络情绪识别方法,方法通过条件格兰杰计算不同情绪下大脑全通 道的因果关系并根据因果关系构建因果网络,分析因果网络中通道的入度出度和介数拓扑属性来找出关键通道,通过关键通 道简化因果网络;最后将简化后的因果网络中节点之间的因果连接关系当作一种特征分别在 SVM 和 KNN 下训练分类。实验 结果表明,平均识别率分别为 75.3%和 78.4%,提出的方法是有效的。

Abstract: Emotions are produced by multiple channels in the brain, but when calculating the causal relationship between any two channels, the influence of other channels is often ignored. Therefore, the introduction of conditional Granger proposes a causal network emotion recognition method based on multi-channel EEG signals. The method uses conditional Granger to calculate the causal relationship of the whole brain channels under different emotions and construct a causal network based on the causal relationship, and analyze the in-degree and out-degree and betweenness topological properties of the channels in the causal network to find the key channel, and simplify the causality through the key channel. Finally, the causal connection relationship between nodes in the simplified causal network is used as a feature to train and classify under SVM and KNN. Experimental results show that the average recognition rates are 75.3% and 78.4%, respectively, and the proposed method is effective.