脑电情感识别中多上下文向量优化的卷积递归神经网络

Convolutional recurrent neural network optimized by multiple context vectors in EEG-based emotion recognition

目前的脑电(EEG)情感识别模型忽略了不同时段情感状态的差异性,未能强化关键的情感信息。针对上述问题,提出一种多上下文向量优化的卷积递归神经网络(CR-MCV)。首先构造脑电信号的特征矩阵序列,通过卷积神经网络(CNN)学习多通道脑电的空间特征;然后利用基于多头注意力的递归神经网络生成多上下文向量进行高层抽象特征提取;最后利用全连接层进行情感分类。在DEAP(Database for Emotion Analysis using Physiological signals)数据集上进行实验,CR-MCV在唤醒和效价维度上分类准确率分别为88.09%和89.30%。实验结果表明,CR-MCV在利用电极空间位置信息和不同时段情感状态显著性特征基础上,能够自适应地分配特征的注意力并强化情感状态显著性信息。

Existing emotion recognition models based on Electroencephalogram(EEG)almost ignore differences in emotional states at different time periods,and fail to reinforce key emotional information.To solve the above problem,a Multiple Context Vectors optimized Convolutional Recurrent neural network(CR-MCV)was proposed.Firstly,feature matrix sequence of EEG signals was constructed to obtain spatial features of multi-channel EEG by Convolutional Neural Network(CNN).Then,recurrent neural network based on multi-head attention was adopted to generate multiple context vectors for high-level abstract feature extraction.Finally,a fully connected layer was used for emotion classification.Experiments were carried out on DEAP(Database for Emotion Analysis using Physiological signals)dataset,and the classification accuracy in arousal and valence dimensions was 88.09%and 89.30%,respectively.Experimental results show that the CR-MCV can adaptively allocate attention of features and strengthen salient information related to emotion states based on utilization of electrode spatial position information and saliency characteristics of emotional states at different time periods.