基于脑肌耦合导联选择和最小生成树网络的脑电特征提取  

EEG feature extraction based on corticomuscular coupling node selection and minimum spanning tree network

作　　者：商晓锋 罗志增[1] 史红斐 Shang Xiaofeng;Luo Zhizeng;Shi Hongfei(Inteligent Control and Robot Research Institute,Hanghou Dianzi Universiy,Hanghou 310018,China;TheFourth Afiliated Hospital Zhejiang University School of Medicine,Jinhua 322000,China)

机构地区：[1]杭州电子科技大学智能控制与机器人研究所,杭州310018 [2]浙江大学医学院附属第四医院,金华322000

出　　处：《仪器仪表学报》2022年第7期191-198,共8页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(62171171);浙江省教育厅一般科研项目(Y202146756)资助。

摘　　要：针对主动康复训练中的运动想象脑电识别问题,提出一种基于脑肌耦合导联选择的双层脑功能网络特征提取方法。根据脑肌耦合强度选择受试各动作下的核心导联,运用核心导联并结合神经生理学中关于运动感觉脑区的先验知识,构建运动感觉核心导联区域网络并提取特征。利用最小生成树全网络特点,将最小生成树脑网络的直径和平均离心率,以及核心导联区域网络的平均节点度、平均聚类系数和平均路径长度,构筑最小生成树脑网络和核心导联区域网络相结合的全局和区域脑功能网络综合特征。选择支持向量机为分类方法,两类运动想象识别的平均正确率为86.96%,证实了本文所提双层脑功能网络分析方法有优良的特征表达能力,能有效提取神经-肌肉内在关联特征,为运动想象识别提供了一种新的思路。There is the problem of motor imagery recognition in rehabilitation training. To address this issue, a feature extraction method of bilevel brain functional network based on the corticomuscular coupling node selection is proposed. According to the strength of corticomuscular coupling, the core nodes under each movement of the subjects are selected. Based on the core nodes and the prior knowledge of motor sensory brain region in neurophysiology, the motor sensory core node regional network is constructed and the features are extracted. By utilizing the whole network characteristic of minimum spanning tree, the diameter and average eccentricity of the minimum spanning tree are combined with the average node degree, average clustering coefficient and average path length of the core brain network. In this way, the comprehensive characteristics of global and regional functional brain network are constructed by the bilevel brain functional network. The support vector machine is selected as the classification method, and the average accuracy of two types motion imagery is 86.96%, which confirms that the proposed bilevel brain functional network analysis method has excellent feature expression ability and can effectively extract the inherent neural-muscle correlation features. It provides a new idea for motor imagination recognition.

关 键 词：脑肌耦合 最小生成树 脑功能网络 运动想象 

分 类 号：TH89[机械工程—仪器科学与技术]