南京大学学报(自然科学版) ›› 2020, Vol. 56 ›› Issue (4): 570580.doi: 10.13232/j.cnki.jnju.2020.04.015
Zhaoyang Li1,2,Anmin Gong4,Yunfa Fu1,2,3()
摘要:
基于想象的脑机接口(Brain?Computer Interface,BCI)在运动障碍康复中有潜在的应用.传统的想象任务是运动想象(Motor Imagery,MI),但MI不易习得和控制,且存在“BCI(Brain Computer Interface)盲”现象,使得该类BCI的实用化受限.为寻找下肢运动障碍的康复方法,采用一种较少被研究且易完成的心理想象,即“视觉想象(Visual Imagery,VI)”来构建BCI,但该类BCI的分类难度较大,需要探索有效的特征提取方法.招募18名被试参加两种动态图片的视觉想象任务并采集脑电(Electroencephalogram,EEG)数据;采用EEG互信息构建功能网络,利用图论分析方法计算脑网络的网络属性特征,分别以网络属性特征、不同维度邻接矩阵空间特征与网络属性与邻接矩阵组合特征构建特征向量;最后采用支持向量机(Support Vector Machine,SVM)对两类视觉想象任务进行分类.结果显示,采用八维互信息邻接矩阵构建的空间特征集具有较好的可分性,平均分类精度为90.12%
中图分类号:
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