随着机器学习技术的快速发展，深度学习等系列算法在一维生理信号处理方面得到了广泛的应用。本文针对脑电（EEG）信号，使用深度学习开源框架中的深度信念网络（DBN）模型识别积极、消极、中性 3 种情绪状态，并与支持向量机（SVM）进行识别效率的对比，通过采集受试者在不同情绪刺激状态下的脑电信号，利用深度信念网络和支持向量机分别对基于不同特征变换和不同频段的情绪表征数据进行识别。研究结果发现，利用深度信念网络对差分熵（DE）特征进行识别的平均准确率为 89.12%±6.54%，与之前的研究相比在同一批数据集上的识别效果更好，同时深度信念网络的分类效果在数值上好于传统的支持向量机（平均分类准确率为 84.2%±9.24%），其准确率和稳定性都有相应更好的趋势，另外受试者在 3 次重复试验中都能得到比较一致的分类准确率（标准差的平均值为 1.44%），试验结果较为稳定，试验具有一定的可重复性。研究结果显示，差分熵特征相比于其他特征在分类器中有着更好的分类准确率，此外，方法中使用 Beta 频段和 Gamma 频段在情绪识别模型中有着更好的分类效果。综上所述，利用深度学习算法进行情绪识别，能够在准确率上有所提升，对于建立能够更准确地识别情绪状态的辅助识别系统有着一定的借鉴意义。此外，本文研究结果进一步提示可以通过分类结果反演找出与情绪状态最相关的脑区和频段，从而有利于加深对于情绪机制的理解，因此本文在利用脑电信号表征情绪状态的识别研究领域具有一定的学术价值和应用价值，值得更深入的探究。
In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.