为了提高脑电情绪识别分类精度,最大限度利用脑电信号的空间和时间信息,提出一种Inception残差注意力卷积神经网络与双向长短期记忆(bi-directional long short-term memory,BiLSTM)网络相结合的新型架构时空In-ception 残差注意力网络.将脑电信号采集电极位置映射到二维矩阵中,采集信号作为通道,构成三维数据;将得到的三维数据输入到时空Inception残差注意力卷积网络之中,提取时空信息;将得到的特征输入到全连接层进行分类;将Inception结构引入脑电情绪识别领域,实现多尺度特征提取,并将电极映射到矩阵之中,保留电极位置信息,使用时空Inception残差注意力网络从时空两个维度获取脑电相关信息.实验表明,使用该模型对DEAP数据集进行情绪四分类可得到93.71％的准确度,相较于对比模型,识别精度提高了 10％～20％.提出的模型在脑电信号情绪识别领域具有优良性能.
EEG emotion recognition based on spatiotemporal inception residual attention network
In order to improve the classification accuracy of electroencephalogram(EEG)emotion recognition and maxi-mize the use of spatial and temporal information of EEG signals,a novel architecture spatio-temporal Inception residual at-tention convolutional neural network combined with bi-directional long short-term memory(BiLSTM)network is proposed.The electrode positions of EEG signals are mapped into a two-dimensional matrix,and the acquired signals are used as channels to form three-dimensional data;the obtained three-dimensional data are inputted into the spatio-temporal Inception residual attention convolutional network to extract the spatio-temporal information;and the obtained features are inputted in-to the fully connected layer for classification.In this paper,the Inception structure is introduced into the field of EEG emo-tion recognition,multi-scale feature extraction is realized,and the electrodes are mapped into the matrix,the electrode po-sition information is retained,and the spatio-temporal Inception residual attention network is used to obtain the EEG-related information from the spatio-temporal dimension.Experiments show that 93.71％accuracy can be obtained by using the mod-el for emotion Ⅳ classification of DEAP dataset,and the recognition accuracy is improved by 10％～20％compared with the comparison model.The proposed model has excellent performance in the field of EEG signal emotion recognition.
万方数据
维普
