基于卷积联合适应网络的脑电信号情感识别  

EEG Emotion Recognition Based on Convolutional Joint Adaptation Network

作　　者：陈景霞[1] 胡修文 唐喆喆 刘洋[1] 胡凯蕾 CHEN Jingxia;HU Xiuwen;TANG Zhezhe;LIU Yang;HU Kailei(College of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an 710021,China)

机构地区：[1]陕西科技大学电子信息与人工智能学院,西安710021

出　　处：《数据采集与处理》2022年第4期814-824,共11页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61806118);陕西科技大学科研启动基金(2020BJ-30)。

摘　　要：提出一种基于深度卷积联合适应网络(Convolutional neural network⁃joint adaptation network,CNN⁃JAN)的脑电信号(Electroencephalogram,EEG)情感识别模型。该模型将迁移学习中联合适应的思想融合到深度卷积网络中,首先采用长方形卷积核提取数据的空间特征,捕捉脑电数据通道间的深层情感相关信息,再将提取的空间特征输入含有联合分布的多核最大均值差异算法(Multi⁃kernel joint maximum mean discrepancy,MK⁃JMMD)的适配层进行迁移学习,使用MK⁃JMMD度量算法解决源域和目标域分布不同的问题。所提方法在SEED数据集上使用微分熵特征和微分尾端性特征分别进行情感分类实验,其中使用微分熵特征被试内跨试验准确率达到84.01%,与对比实验和目前流行的迁移学习方法相比,准确率进一步提高,跨被试实验精度也取得较好的性能,验证了该模型用于EEG信号情感识别任务的有效性。A new electroencephalogram(EEG)emotion recognition method based on deep convolutional neural network⁃joint adaptation network(CNN-JAN)is presented.It incorporates the idea of joint adaptation in transfer learning into deep convolutional networks.Firstly,the model uses a rectangular convolution kernel to extract the deep emotion-related spatial features between EEG data channels.Then,the extracted spatial features are input into the adaptation layer with multi-kernel joint maximum mean discrepancy(MK-JMMD)for transfer learning,aiming to reduce the distribution differences between the source and target domains.The experiments are carried out on differential entropy features and differential causality features of EEG data from the SEED dataset to verify the effectiveness and advantages of the proposed method.As a result,the within-subject emotion classification accuracy on differential entropy features reaches 84.01%,and the cross-subject emotion classification accuracy is also improved compared with other current popular transfer learning methods.

关 键 词：脑电信号 卷积神经网络 迁移学习 情感识别 联合适应网络 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]