基于脑电多域特征融合的跨任务认知负荷研究  

Cross-task cognitive load based on EEG multidomain feature fusion

作　　者：宋雨[1,2] 刘杨 高强[1,2,3] 刘俊杰 李荭娜[3] 吉月辉 SONG Yu;LIU Yang;GAO Qiang;LIU Junjie;LI Hongna;JI Yuehui(School of Electrical Engineering and Automation,Tianjin University of Technology,Tianjin 300384,China;Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems,Tianjin University of Technology,Tianjin300384,China;Maritime College,Tianjin University of Technology,Tianjin 300384,China)

机构地区：[1]天津理工大学电气工程与自动化学院,天津300384 [2]天津理工大学天津市复杂系统控制理论及应用重点实验室,天津300384 [3]天津理工大学海运学院,天津300384

出　　处：《实验技术与管理》2024年第2期73-80,共8页Experimental Technology and Management

基　　金：天津市普通高等学校本科教学改革与质量建设研究计划项目(A231006001);天津市研究生科研创新项目(2022SKYZ252);天津理工大学教学基金(ZD22-06);天津理工大学研究生教学基金(ZDXM2202)。

摘　　要：为探究不同认知负荷下的人脑活动,设计了三种实验范式(N-Back、心算和Sternberg),采集被试者在三类认知负荷下的脑电图(electroencephalogram,EEG)信号,并对EEG信号进行预处理、特征提取和特征分类。模型通过相位锁相值(phase locking value,PLV)计算了EEG的功能连接特征,将PLV功能连接矩阵作为脑功能网络的边;以微分熵特征作为网络的节点信息,实现EEG频域与空间域特征的融合,利用图注意力神经网络完成了跨任务的认知负荷分类。模型在跨任务三分类认知负荷识别中取得了57.12%的平均分类准确率。基于复杂网络理论,从全局和局部两个层次分析了不同负荷状态下大脑网络结构的变化,随着认知负荷程度增加,theta与alpha频段的全局聚类系数逐渐减小,delta与theta频段的全局效率则有所提高;theta频段下的额叶、顶叶与颞叶脑区电极的局部效率呈上升趋势。网络全局与局部的度量变化表明随着人脑认知负荷程度的提高,功能脑网络的拓扑结构在发生改变。[Objective]At present,most experimental results in cognitive load research focus on a single psychological task,with limited exploration and expansion of cross-task cognitive load identification.To explore human brain activity under different cognitive loads,electroencephalogram(EEG)signals were collected from various tasks.Cross-task cognitive load recognition was achieved by fusing frequency domain and spatial domain features,and the functional brain network of EEG signals was visualized and analyzed based on graph theory.[Methods]Three experimental paradigms(N-Back,mental arithmetic,and Sternberg)were designed to collect EEG signals from participants under three types of cognitive loads.The original EEG signals were preprocessed through operations such as downsampling,filtering,rereferencing,and independent component analysis to remove noise from the original data.The feature extraction method was applied to extract features,including differential entropy(DE),power spectral density(PSD),and the phase locking value(PLV),in terms of frequency and spatial domains.The degree to which each feature represents cognitive load was evaluated.Moreover,a functional brain network architecture was constructed based on the graph theory and phase lag values.The frequency domain features and functional brain network were fused through graph attention networks(GATs),and the brain network topology was efficiently learned through attention modules.The network parameters of the functional brain network were calculated using the graph theory,including global clustering coefficients,global efficiency,and local efficiency.Differences in functional brain networks were analyzed under different loads based on statistical changes in network parameters.[Results]The results of the cross-task cognitive load show that:①DE has a stronger classification performance than PSD,and PLV has a better classification performance than traditional frequency domain features.The classification accuracy based on a single feature can reach up to 49.6%.②

关 键 词：脑电信号 认知负荷 跨任务 图注意力神经网络 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]