Yan Jianzhuo, Chen Shangbin, Deng Sinuo
Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, 100124, China.
Brain Inform. 2019 Sep 23;6(1):7. doi: 10.1186/s40708-019-0100-y.
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.
作为人类大脑的高级功能，情绪对人类的学习、工作及生活的其他方面有着重大影响。人工智能在正确识别人类情绪方面发挥了重要作用。基于脑电图的情绪识别（ER）作为脑机接口（BCI）的一种应用，近年来越来越受欢迎。然而，由于人类情绪的模糊性和脑电图信号的复杂性，能够高精度识别情绪的脑电图 - ER系统并不容易实现。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点。根据脑电图的节律特征和时间记忆特征，本研究提出了一种基于长短期记忆网络（LSTM）的效价和唤醒度的节律性时间脑电图情绪识别模型（RT - ERM）。通过应用该模型，不同节律和时间尺度的分类结果有所不同。通过不同节律和不同时间尺度的分类准确率结果获得RT - ERM模型的最优节律和时间尺度。然后，通过不同节律对应的最佳时间尺度对情绪脑电图进行分类。最后，通过与其他现有的情绪脑电图分类方法进行比较，发现该模型的节律和时间尺度有助于提高RT - ERM的准确率。
