Wang Jiaheng, Wang Yueming, Yao Lin
School of Computer Science, Zhejiang Universty, Hangzhou 310000, P.R.China.
Frontiers Science Center for Brain & Brain-machine Integration, Zhejiang Universty, Hangzhou 310000, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Jun 25;38(3):447-454. doi: 10.7507/1001-5515.202012054.
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.
情感在人们的认知和交流中起着重要作用。通过分析脑电图（EEG）信号来识别内在情感，并以主动或被动的方式反馈情感信息，情感脑机交互能够有效地促进人机交互。本文聚焦于使用EEG进行情感识别。我们使用一个用于基于生理信号的情感分析的公开可用数据集（DEAP），系统地评估了当前最先进的特征提取和分类方法的性能。常见的随机分割方法会导致训练样本和测试样本之间的高度相关性。因此，我们使用分块折交叉验证。此外，我们比较了不同时间窗口长度下情感识别的准确率。实验结果表明，4秒的时间窗口适合采样。提出了以微分熵特征作为输入的滤波器组长短期记忆网络（FBLSTM）。在效价维度、唤醒维度以及效价-唤醒平面中四个维度的组合方面，低和高的平均准确率分别为78.8%、78.4%和70.3%。这些结果证明了我们的情感识别模型在分类准确率方面优于当前的研究。我们的模型可能为情感脑机交互中的情感识别提供一种新方法。
