PDF(Pubmed)
Abstract：
Recognizing the emotional states of humans through EEG signals are of great significance to the progress of human-computer interaction. The present study aimed to perform automatic recognition of music-evoked emotions through region-specific information and dynamic functional connectivity of EEG signals and a deep learning neural network. EEG signals of 15 healthy volunteers were collected when different emotions (high-valence-arousal vs. low-valence-arousal) were induced by a musical experimental paradigm. Then a sequential backward selection algorithm combining with deep neural network called Xception was proposed to evaluate the effect of different channel combinations on emotion recognition. In addition, we also assessed whether dynamic functional network of frontal cortex, constructed through different trial number, may affect the performance of emotion cognition. Results showed that the binary classification accuracy based on all 30 channels was 70.19%, the accuracy based on all channels located in the frontal region was 71.05%, and the accuracy based on the best channel combination in the frontal region was 76.84%. In addition, we found that the classification performance increased as longer temporal functional network of frontal cortex was constructed as input features. In sum, emotions induced by different musical stimuli can be recognized by our proposed approach though region-specific EEG signals and time-varying functional network of frontal cortex. Our findings could provide a new perspective for the development of EEG-based emotional recognition systems and advance our understanding of the neural mechanism underlying emotion processing.
摘要：
通过脑电信号识别人类的情绪状态对人机交互的发展具有重要意义。本研究旨在通过特定区域的信息和脑电信号的动态功能连接以及深度学习神经网络来自动识别音乐诱发的情绪。收集15名健康志愿者不同情绪时的脑电信号(高心价-唤醒与低价唤醒）是由音乐实验范式引起的。然后提出了一种与深度神经网络相结合的序列后向选择算法Xception来评估不同通道组合对情感识别的影响。此外,我们还评估了额叶皮质的动态功能网络，通过不同的试验编号构建，可能会影响情绪认知的表现。结果表明，基于所有30个通道的二元分类准确率为70.19%，基于位于额叶区域的所有通道的准确度为71.05%，基于正面区域最佳通道组合的准确性为76.84％。此外,我们发现，随着额叶皮层更长的时间功能网络被构建为输入特征，分类性能会提高。总之,通过我们提出的方法，可以通过特定区域的EEG信号和额叶皮层的时变功能网络来识别不同音乐刺激引起的情绪。我们的发现可以为基于EEG的情感识别系统的发展提供新的视角，并促进我们对情感处理背后的神经机制的理解。
