Emotion recognition has become a research focus in the brain–computer interface and cognitive neuroscience. Electroencephalogram (EEG) is employed for its advantages as accurate, objective, and noninvasive nature. However, many existing research only focus on extracting the time and frequency domain features of the EEG signals while failing to utilize the dynamic temporal changes and the positional relationships between different electrode channels. To fill this gap, we develop the dynamic differential entropy and brain connectivity features based EEG emotion recognition using linear graph convolutional network named DDELGCN. First, the dynamic differential entropy feature which represents the frequency domain feature as well as time domain feature is extracted based on the traditional differential entropy feature. Second, brain connectivity matrices are constructed by calculating the Pearson correlation coefficient, phase-locked value and transfer entropy, and then are used to denote the connectivity features of all electrode combinations. Finally, a linear graph convolutional network is customized and applied to aggregate the features from total electrode combinations and then classifies the emotional states, which consists of five layers, namely, an input layer, two linear graph convolutional layers, a fully connected layer, and a softmax layer. Extensive experiments show that the accuracies in the valence and arousal dimensions reach 90.88% and 91.13%, and the precision reaches 96.66% and 97.02% on the DEAP dataset, respectively. On the SEED dataset, the accuracy and precision reach 91.56% and 97.38%, respectively.

中文翻译：

---

基于动态微分熵和脑连接特征的脑电情绪识别

情绪识别已成为脑机接口和认知神经科学的研究热点。脑电图 (EEG) 因其准确、客观和无创性等优点而被采用。然而，现有的许多研究仅侧重于提取脑电信号的时域和频域特征，而未能利用动态时间变化和不同电极通道之间的位置关系。为了填补这一空白，我们使用名为 DDELGCN 的线性图卷积网络开发了基于动态微分熵和大脑连接特征的 EEG 情绪识别。首先，在传统微分熵特征的基础上，提取了代表频域特征和时域特征的动态微分熵特征。第二，通过计算皮尔逊相关系数、锁相值和传递熵构建脑连接矩阵，然后用其表示所有电极组合的连接特征。最后，定制并应用线性图卷积网络聚合所有电极组合的特征，然后对情绪状态进行分类，该网络由五层组成，即输入层、两个线性图卷积层、全连接层和一个softmax层。大量实验表明，效价和唤醒维度的准确率分别达到 90.88% 和 91.13%，在 DEAP 数据集上的准确率分别达到 96.66% 和 97.02%。在SEED数据集上，准确率和精确率分别达到91.56%和97.38%。