基于混合网络和注意力机制的脑电情感识别  

EEG Emotional Recognition Based on Hybrid Network and Attention Mechanism

作　　者：朱飞宇 王杰华[1] 丁卫平[1] 谢天 ZHU Fei-yu;WANG Jie-hua;DING Wei-ping;XIE Tian(School of Information Science and Technology,Nantong University,Nantong Jiangsu 226019,China)

机构地区：[1]南通大学信息科学技术学院,江苏南通226019

出　　处：《计算机仿真》2024年第6期525-530,共6页Computer Simulation

基　　金：国家自然科学基金面上项目(61976120);南通市基础科学研究计划项目(JC2020143)。

摘　　要：针对目前脑电信号情感识别准确率不高,循环模型特征提取能力不足等问题,提出了基于一维卷积和BiBASRU-AT的脑电信号情感识别模型。对数据集进行分段预处理以扩充样本数量,由一维卷积提取62个通道局部情感特征;构建内置自注意力简单循环单元以捕捉多通道融合特征以及通道之间的依赖关系,软注意力机制识别出对情感倾向识别影响较大的重点特征,线性层输出积极、中性、消极的情感识别结果。在脑电信号数据集SEED上的实验结果表明,该模型取得了90.24%的平均分类准确率,高于实验对比的优秀深度学习模型,内置自注意力简单循环单元特征捕捉能力更强,证明了模型的有效性。To address the problems of low accuracy of EEG emotion sentiment and insufficient feature extraction ability of recurrent model,an EEG sentiment recognition model combining one-dimensional convolution and BiBASRU-AT is proposed.The data set is preprocessed in segments to expand the number of samples,and 62 channel local emotional features are extracted from one-dimensional convolution;The built-in self attention simple recurrent unit is constructed to capture the multi-channel fusion features and the dependence between channels.The soft attention mechanism identifies the key features that have a great impact on the identification of emotional tenden⁃cies,and the linear layer outputs the positive,neutral and negative emotion recognition results.The experimental re⁃sults on the EEG data set(SEED)show that the model achieves an average classification accuracy of 90.24%,which is higher than the excellent deep learning model compared with the experiment.The built-in self attention simple re⁃current unit has stronger feature capture ability,which proves the effectiveness of the model.

关 键 词：情感识别 脑电信号 简单循环单元 自注意力 一维卷积 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]