摘要
运动想象MI是基于想象的脑机交互BCI中常用的任务,但MI不易习得和控制,且存在“BCI盲”现象,使得该类BCI的实用化受限。针对较易习得和控制的视觉想象VI任务进行识别,旨在构建基于VI的BCI(VI-BCI)。招募了15名被试者参加2种动态图像的视觉想象任务并采集脑电EEG数据;然后采用EEG微状态方法研究了这2种VI任务诱发的EEG在微状态时间参数上的差异,并选用差异显著的微状态时间参数构建特征向量;最后采用SVM对2类VI任务进行识别。结果显示提取微状态特征所取得的最高、最低和平均分类精度分别为90%,56%和80.6±2.58%。表明微状态方法可以有效提取VI相关EEG特征并得到具有可比性的分类精度,可望为构建相对较新的在线VI-BCI提供思路。
Motor imagery(MI)is a common task in brain computer interaction(BCI),but MI is not easy to acquire and control,and there is a phenomenon of"BCI blindness",which limits the practicality of this type of BCI.This paper aims at the identification of Visual Imagery(VI)tasks that are easier to acquire and control,and aims to build VI-based BCI(VI-BCI).15 subjects were recruited to participate in two kinds of dynamic picture VI tasks,and their EEG data were collected.Then,the EEG microstate method is used to study the differences in microstate time parameters between the two VI tasks,and the eigenvectors are constructed by microstate time parameters with significant differences.Finally,support vector machine(SVM)is used to classify the two kinds of VI tasks.The results show that the highest,the lowest and the average classification accuracy of microstate are 90%,56%and 80.62.58%,respectively.This study shows that the microstate method can effectively extract VI-related EEG features and obtain comparable accuracy.The work is expected to provide ideas for the construction of a new online VI-BCI.
作者
李昭阳
伏云发
LI Zhao-yang;FU Yun-fa(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《计算机工程与科学》
CSCD
北大核心
2021年第3期465-472,共8页
Computer Engineering & Science
基金
国家自然科学基金(8177071438,6176020268)。
关键词
视觉想象
微状态
脑电
脑机交互
visual imagery
microstate
EEG
brain computer interaction
作者简介
李昭阳(1994),男,河南平顶山人,硕士生,研究方向为脑机接口。E-mail:1094413489@qq.com;伏云发(1968),男,云南昆明人,博士,教授,研究方向为脑机接口。E-mail:774169326@qq.com。
