School of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, Xi'an 710062, China.
Sensors (Basel). 2024 Mar 20;24(6):1979. doi: 10.3390/s24061979.
The gated recurrent unit (GRU) network can effectively capture temporal information for 1D signals, such as electroencephalography and event-related brain potential, and it has been widely used in the field of EEG emotion recognition. However, multi-domain features, including the spatial, frequency, and temporal features of EEG signals, contribute to emotion recognition, while GRUs show some limitations in capturing frequency-spatial features. Thus, we proposed a hybrid architecture of convolutional neural networks and GRUs (CGRU) to effectively capture the complementary temporal features and spatial-frequency features hidden in signal channels. In addition, to investigate the interactions among different brain regions during emotional information processing, we considered the functional connectivity relationship of the brain by introducing a phase-locking value to calculate the phase difference between the EEG channels to gain spatial information based on functional connectivity. Then, in the classification module, we incorporated attention constraints to address the issue of the uneven recognition contribution of EEG signal features. Finally, we conducted experiments on the DEAP and DREAMER databases. The results demonstrated that our model outperforms the other models with remarkable recognition accuracy of 99.51%, 99.60%, and 99.59% (58.67%, 65.74%, and 67.05%) on DEAP and 98.63%, 98.7%, and 98.71% (75.65%, 75.89%, and 71.71%) on DREAMER in a subject-dependent experiment (subject-independent experiment) for arousal, valence, and dominance.
门控循环单元 (GRU) 网络可以有效地捕获一维信号（如脑电图和事件相关脑电位）的时间信息，并且已广泛应用于 EEG 情绪识别领域。然而，多域特征，包括 EEG 信号的空间、频率和时间特征，有助于情绪识别，而 GRU 在捕获频率-空间特征方面存在一些局限性。因此，我们提出了卷积神经网络和 GRU 的混合架构 (CGRU)，以有效地捕获隐藏在信号通道中的互补时间特征和空间-频率特征。此外，为了研究情绪信息处理过程中不同脑区之间的相互作用，我们通过引入锁相值来考虑大脑的功能连接关系，以计算 EEG 通道之间的相位差，从而基于功能连接获得空间信息。然后，在分类模块中，我们结合了注意力约束，以解决 EEG 信号特征识别贡献不均匀的问题。最后，我们在 DEAP 和 DREAMER 数据库上进行了实验。结果表明，我们的模型在 DEAP 上的唤醒度、愉悦度和主导度的实验中（在依赖于主体和独立于主体的实验中），分别以 99.51%、99.60%和 99.59%（58.67%、65.74%和 67.05%）以及 98.63%、98.7%和 98.71%（75.65%、75.89%和 71.71%）的显著识别准确率优于其他模型。
