基于改进局部图结构的再认记忆脑电特征提取  

EEG feature extraction of recognition memory based on improved local graph structure

作　　者：王凯[1,2] 顾翔 李文杰 王苏弘[4] 邹凌 Wang Kai;Gu Xiang;Li Wenjie;Wang Suhong;Zou Ling(School of Microelectronics and Control Engineering,Changzhou University,Changzhou 213164,China;Changzhou Key Laboratory of Biomedical Information Technology,Changzhou 213164,China;Aliyun School of Big Data,School of Software,Changzhou University,Changzhou 213164,China;Clinical Psychology,The Third Affiliated Hospital of Soochow University,Changzhou 213003,China)

机构地区：[1]常州大学微电子与控制工程学院,常州213164 [2]常州市生物医学信息技术重点实验室,常州213164 [3]常州大学阿里云大数据学院,常州213164 [4]苏州大学附属第三医院临床心理科,常州213003

出　　处：《电子测量技术》2024年第4期81-86,共6页Electronic Measurement Technology

基　　金：江苏省重点研发计划项目(BE2021012-2,BE2021012-5);常州市科技计划(CE20225034);浙江省脑机协同智能基金重点实验室项目(2020E10010-04)资助。

摘　　要：为了研究再认记忆脑电的纹理特征,以及解决垂直对称局部图结构和对称局部图结构在提取脑电纹理特征时结构不稳定的问题。基于新旧范式设计了再认记忆实验,采集医学生和非医学生(均为35名)相关脑电,并且将这些脑电分为学习医学图片阶段、学习非医学图片阶段、再认旧医学图片阶段、再认旧非医学图片阶段、再认新医学图片阶段和再认新非医学图片阶段。首先,利用二维小波变换得到每位被试脑电的三个子频带,并提出改进集成局部图结构方法对原数据和3个子频带进行特征提取,改进算法纳入了具有稳定结构的扩展对称局部图结构和复合局部图结构;然后对特征进行归一化,避免结果过拟合,使用皮尔逊相关系数筛选出相关系数在0.8~1之间的特征矩阵列。在支持向量机等分类器上验证改进前后的算法,并使用正确率、精确率、召回率和F1评分这四个指标对模型进行评估。与改进前算法相比,改进后算法在支持向量机上的分类正确率分别提升3.8%,0.4%,0.3%,1.6%,5.1%和4.2%。分类结果说明医学生和非医学生在医学图片学习再认阶段存在明显差异,新加入扩展对称局部图结构和复合局部图结构比原算法中垂直对称局部图结构和对称局部图结构具有更好的分类性能。To investigate the texture features of recognition memory EEG and address the issue of structural instability in extracting EEG texture features using vertical symmetric local graph structure(VSLGS)and symmetric local graph structure(SLGS).A recognition memory experiment was designed based on new and old paradigms,and relevant EEG data were collected from 35 medical students and 35 non-medical students.The EEG data were categorized into six different stages:learning medical images,learning non-medical images,recognizing old medical images,recognizing old non-medical images,recognizing new medical images,and recognizing new non-medical images.Firstly,a 2D wavelet transform was applied to obtain three subbands for each participant′s EEG data.Then,an improved integrated local graph structure method was proposed to extract features from the original data and the three subbands.This improved algorithm incorporated extended symmetric local graph structures(ESLGS)and composite local graph structures(CLGS)with stable structures.The features were then normalized to avoid overfitting,and feature matrix columns with correlation coefficients between 0.8 and 1 were selected using Pearson correlation coefficients.The improved algorithm was validated on classifiers such as support vector machines(SVM),and the model was evaluated using four metrics:accuracy,precision,recall,and F1 score.Compared to the original algorithm,the improved algorithm achieved an increase of 3.8%,0.4%,0.3%,1.6%,5.1%,and 4.2%in classification accuracy on support vector machines for each condition.The classification results indicate significant differences between medical students and non-medical students in the recognition stage of learning medical images.The addition of ESLGS and CLGS demonstrates better classification performance compared to the original algorithm,which utilized VSLGS and SLGS.

关 键 词：脑电 再认记忆 集成局部图结构 特征提取 

分 类 号：TP331[自动化与计算机技术—计算机系统结构]