As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.

中文翻译：

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基于脑电图的具有节奏和时间特征的情绪识别模型。

作为人类大脑的高级功能，情感对人类的研究，工作和生活的其他方面具有重大影响。人工智能在正确识别人类情感方面发挥了重要作用。基于脑电图的情绪识别（ER）是脑计算机接口（BCI）的一种应用，近年来变得越来越流行。然而，由于人类情感的含混性和EEG信号的复杂性，难以实现高精度识别情感的EEG-ER系统。基于时间尺度，本文选择递归神经网络作为筛选模型的突破口。根据脑电图的节奏特征和时间记忆特征，这项研究提出了一种基于长短时记忆网络（LSTM）的价和唤醒的节律性EEG情绪识别模型（RT-ERM）。通过应用该模型，不同节奏和时间尺度的分类结果是不同的。通过不同节奏和不同时标的分类精度结果，可以得到RT-ERM模型的最佳节奏和时标。然后，通过对应于不同节奏的最佳时标对情绪性脑电图进行分类。最后，通过与其他现有的情感脑电分类方法进行比较，发现该模型的节奏和时间尺度可以有助于RT-ERM的准确性。通过不同节奏和不同时标的分类精度结果，可以得到RT-ERM模型的最佳节奏和时标。然后，通过对应于不同节奏的最佳时标对情绪性脑电图进行分类。最后，通过与其他现有的情感脑电分类方法进行比较，发现该模型的节奏和时间尺度可以有助于RT-ERM的准确性。通过不同节奏和不同时标的分类精度结果，可以得到RT-ERM模型的最佳节奏和时标。然后，通过对应于不同节奏的最佳时标对情绪性脑电图进行分类。最后，通过与其他现有的情感脑电分类方法进行比较，发现该模型的节奏和时间尺度可以有助于RT-ERM的准确性。