基于多重有向加权图与卷积神经网络的脑电情感识别

EEG Emotion Recognition Based on Multiple Directed Weighted Graph and Convolutional Neural Network

脑电信号应用于情感识别的研究近年来受到广泛的关注,将时间序列映射为可见图表示,通过可见图边和节点的度量能够有效进行脑电情感识别。传统可见图算法忽略了多通道脑电信号间的相关性,难以保留时间序列上复杂的特征信息。因此提出一种多重有向加权可见图提取脑电信号特征的方法,并将脑电信号特征用于情感识别。首先将脑电信号转换成有向加权网络图,强化信号的特征表示,使用加权聚类系数表征复杂网络结构,以多重复杂网络建立脑电连接矩阵,最后采用卷积神经网络进行特征学习,通过学习后获得情感识别结果。多重有向加权可见图构建的复杂网络模型在公开数据集验证的准确率达到了93.85%,优于现有的简单可见图方法;多重加权可见图与单变量可见图相比,情感识别准确率提高了9.4%。实验结果表明,该方法在跨被试者数据上同样适用,具有很好的鲁棒性。

In recent years,research on EEG signals applied to emotion recognition has received extensive attention,and mapping time series into visibility graph representation can effectively perform EEG emotion recognition through the metric of edges and nodes of the visibility graph.Traditional visibility graph algorithms ignore the correlation between multi-channel EEG signals,and it is difficult to retain the complex feature information on the time series.Therefore,this paper proposes a method to extract EEG signal features from multiple directed weighted visibility graphs and use EEG signal features for emotion recognition.Firstly,the EEG signal is converted into a directed weighted network graph to enhance the feature representation of the signal,the complex network structure is characterized using weighted clustering coefficients,the EEG connection matrix is established with multiple complex networks,and finally the convolutional neural network is used for feature learning,and the emotion recognition results are obtained through learning.The complex network model constructed by multiple directed weighted visibility maps achieves 93.85%accuracy in public dataset validation,which is better than the existing traditional visibility graph methods,and the multiple weighted visibility graph improves the emotion recognition accuracy by 9.4%compared with univariate visibility graph.Experimental results show that the method is also applicable to cross subject data and has good robustness.