In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.

本研究使用机器学习方法研究了情感识别过程中EEG的稳定模式，并对其在时间上的一致性进行了系统评估，结果显示稳定模式在不同session之间呈现出一致性，正面情绪比负面情绪更容易激活侧向颞区的beta和gamma波，中性情绪在顶部和枕部有更高的alpha波响应，而负面情绪在顶部和枕部有显著更高的delta波响应和前额部位更高的gamma波响应，因此神经模式在情感识别中是相对稳定的。

从 EEG 中鉴定时间稳定的情感识别模式