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  • Title: [Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks].
    Author: Wang J, Wang Y, Yao L.
    Journal: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2021 Jun 25; 38(3):447-454. PubMed ID: 34180189.
    Abstract:
    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 K 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. 情绪在人们的认知、交往等各方面发挥着重要作用，而情绪脑机接口通过分析脑电图（EEG）可识别内在情绪，以主动或被动的方式反馈情绪信息，有效促进人机交互。本文聚焦于 EEG 信号的情绪识别，使用生理信号情绪数据集（DEAP）系统地比对了主流特征提取算法、分类器模型。通常的随机取样方法会造成训练和测试样本相关性高，本文采用分块化 K 交叉验证评估模型，同时对比了不同时间窗长度下的情绪识别准确率，研究表明 4 s 时间窗为适宜的取样时长。此外，本文提出了滤波器组长短时记忆网络（FBLSTM），以微分熵特征作为输入，所提出的算法模型在情绪的效价度二分类、唤醒度二分类、效价—唤醒平面四分类上的平均分类准确率分别为 78.8%、78.4%、70.3%。相比于目前的研究成果，本文的情绪识别模型具有更优的分类性能，或可为情绪脑机接口中的情绪识别提供一种新的可靠方法。.
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