Classification of emotional brain networks based on weighted K-order propagation number
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摘要: 脑电信号与人类情绪具有强相关性，情绪脑网络的节点重要性研究为分析情绪脑机制提供了有效手段。本文采用一种新的节点重要性排序方法——加权 K-阶传播数法，设计实现了一种情绪脑网络的分类算法。首先基于 DEAP 情绪脑电数据构建互样本熵脑网络，对正、负情绪下的脑网络分别进行节点重要性排序，以获得多阈值尺度下的特征矩阵。然后通过特征提取和支持向量机实现对情绪的二分类，分类准确率达到 83.6%。结果表明采用加权 K-阶传播数法提取脑网络节点重要性特征进行情绪分类研究是有效的，为复杂网络的特征提取和分析提供了一种新的方法。Abstract: Electroencephalography (EEG) signals are strongly correlated with human emotions. The importance of nodes in the emotional brain network provides an effective means to analyze the emotional brain mechanism. In this paper, a new ranking method of node importance, weighted K-order propagation number method, was used to design and implement a classification algorithm for emotional brain networks. Firstly, based on DEAP emotional EEG data, a cross-sample entropy brain network was constructed, and the importance of nodes in positive and negative emotional brain networks was sorted to obtain the feature matrix under multi-threshold scales. Secondly, feature extraction and support vector machine (SVM) were used to classify emotion. The classification accuracy was 83.6%. The results show that it is effective to use the weighted K-order propagation number method to extract the importance characteristics of brain network nodes for emotion classification, which provides a new means for feature extraction and analysis of complex networks.
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图 1 加权 K-阶传播数的情绪脑网络分类算法步骤
Figure 1. The flow chart of emotional brain network classification algorithm based on weighted K-order propagation number
图 4 负向情绪脑网络
a. 邻接矩阵 A；b. 0-1 矩阵 B
Figure 4. Negative emotional brain network
a. adjacency matrix A; b. 0-1matrix B
图 5 某被试正向情绪脑网络在 T3、T10、T15、T18 阈值下的网络拓扑图
Figure 5. Network topology of a positive emotional brain network under the threshold T3, T10, T15, and T18
图 6 某被试负向情绪脑网络在 T3、T10、T15、T18 阈值下的网络拓扑图
Figure 6. Network topology of a negative emotional brain network under the threshold T3, T10, T15, and T18
图 7 某被试在 T13下的脑网络节点重要性示意图
Figure 7. The importance map of the brain network node corresponding to a subject under the threshold T13
表 1 每名被试的 DEAP 数据集的数据组成
Table 1. DEAP dataset representation for each subject
类别 数据维度 数据意义 情绪数据 $40 \times 40 \times 8\;064$ 视频 × 导联 × 采样点 标签 $40 \times 4$ 视频 × 标签（效价、唤醒度、
喜欢度、优势度）
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表 2 不同模型分类准确率结果比较
Table 2. Comparison between classification accuracies of different models
模型 SVD PCA KNN 61.8% 66.2% 决策树 51.5% 58.8% SVM 75.3% 83.6%
下载: 导出CSV
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