基于复杂网络的情感脑电相位同步性分析
发布时间：2018-12-29 17:07
【摘要】：使用相位锁值(Phase locking value,PLV)来量化任意两个电极通道之间的相位同步性,构建相应的脑功能网络的关联矩阵,提取网络不同稀疏度下的度、中间中心度两个局部属性的曲线下面积作为特征,对不同类型情感的网络特征进行非参数检验,找出显著性的节点。同时采用得到的特征值作为分类依据,训练SVM分类器。实验表明,利用PLV相位同步方法得到功能网络的局部属性,可以有效地区分不同类型的情感脑电数据,为基于脑电数据的情感识别提供了一种有效的方法。
[Abstract]:Phase locking value (Phase locking value,PLV) is used to quantify the phase synchronization between any two electrode channels, to construct the correlation matrix of the corresponding brain functional network, and to extract the degree of the network with different sparsity. The area under the curve of the two local attributes of the center degree is used as a feature to test the network features of different types of emotions and to find out the significant nodes. At the same time, the obtained eigenvalues are used as the classification basis to train the SVM classifier. The experimental results show that using PLV phase synchronization method to obtain the local attributes of the functional network can effectively distinguish different types of emotional EEG data and provide an effective method for emotion recognition based on EEG data.
【作者单位】： 太原理工大学计算机科学与技术学院计算机科学系;
【基金】：国家自然科学基金(No.61472270,No.61373101)
【分类号】：O157.5;R318
本文编号：2395110
[Abstract]:Phase locking value (Phase locking value,PLV) is used to quantify the phase synchronization between any two electrode channels, to construct the correlation matrix of the corresponding brain functional network, and to extract the degree of the network with different sparsity. The area under the curve of the two local attributes of the center degree is used as a feature to test the network features of different types of emotions and to find out the significant nodes. At the same time, the obtained eigenvalues are used as the classification basis to train the SVM classifier. The experimental results show that using PLV phase synchronization method to obtain the local attributes of the functional network can effectively distinguish different types of emotional EEG data and provide an effective method for emotion recognition based on EEG data.
【作者单位】： 太原理工大学计算机科学与技术学院计算机科学系;
【基金】：国家自然科学基金(No.61472270,No.61373101)
【分类号】：O157.5;R318
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