基于滤波器组长短时记忆网络的脑电信号情绪识别

Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks

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。

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 shortterm 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.