Niu Weixin, Ma Chao, Sun Xinlin, Li Mengyu, Gao Zhongke
IEEE Trans Neural Syst Rehabil Eng. 2023;31:917-925. doi: 10.1109/TNSRE.2023.3236434. Epub 2023 Feb 3.
Constructing reliable and effective models to recognize human emotional states has become an important issue in recent years. In this article, we propose a double way deep residual neural network combined with brain network analysis, which enables the classification of multiple emotional states. To begin with, we transform the emotional EEG signals into five frequency bands by wavelet transform and construct brain networks by inter-channel correlation coefficients. These brain networks are then fed into a subsequent deep neural network block which contains several modules with residual connection and enhanced by channel attention mechanism and spatial attention mechanism. In the second way of the model, we feed the emotional EEG signals directly into another deep neural network block to extract temporal features. At the end of the two ways, the features are concatenated for classification. To verify the effectiveness of our proposed model, we carried out a series of experiments to collect emotional EEG from eight subjects. The average accuracy of the proposed model on our emotional dataset is 94.57%. In addition, the evaluation results on public databases SEED and SEED-IV are 94.55% and 78.91%, respectively, demonstrating the superiority of our model in emotion recognition tasks.
近年来，构建可靠且有效的人类情绪状态识别模型已成为一个重要问题。在本文中，我们提出了一种结合脑网络分析的双向深度残差神经网络，它能够对多种情绪状态进行分类。首先，我们通过小波变换将情绪脑电信号转换为五个频段，并通过通道间相关系数构建脑网络。然后，将这些脑网络输入到一个后续的深度神经网络模块中，该模块包含几个具有残差连接的模块，并通过通道注意力机制和空间注意力机制进行增强。在模型的第二种方式中，我们将情绪脑电信号直接输入到另一个深度神经网络模块中以提取时间特征。在两种方式的最后，将特征连接起来进行分类。为了验证我们提出的模型的有效性，我们进行了一系列实验，从八名受试者那里收集情绪脑电数据。所提出的模型在我们的情绪数据集上的平均准确率为94.57%。此外，在公共数据库SEED和SEED-IV上的评估结果分别为94.55%和78.91%，证明了我们的模型在情绪识别任务中的优越性。
