文献类型：期刊文献

中文题名：基于多相关性的导联前向搜索算法用于运动想象分类

英文题名：Channel Selection Based on Multi-Correlation Forward Searching Algorithm for MI Classification

作者：殷飞宇[1];金晶[1];王行愚[1]

机构：[1]华东理工大学信息科学与工程学院,化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2020

卷号：46

期号：6

起止页码：792

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家重点研发计划(2017YFB13003002);国家自然科学基金(61573142,61773164,91420302);111引智计划(B17017)。

语种：中文

中文关键词：脑-机接口;运动想象;多相关性算法;导联选择;共空间模式

外文关键词：brain-computer interface;motor imagery;multi-correlation algorithm;channel selection;common spatial pattern

摘要：针对基于运动想象(Motor Imagery, MI)的脑-机接口(Brain-Computer Interface, BCI)系统中导联过多的问题,提出了一种多相关性导联前向搜索(Multi-correlationForward Searching, MCFS)算法来优化导联集,改善系统性能。首先基于训练集对导联集进行前向搜索,同时以验证集分类精度更新对3种相关性算法的信任值;然后根据3种相关性方法的信任值,选择优质导联组合,采用共空间模式(Common Spatial Pattern, CSP)获得运动想象特征,通过线性核的支持向量机(Support Vector Machine, SVM)训练分类模型。对该算法在两个数据集(BCI竞赛Ⅳ中的data set Ⅰ数据集Ⅰ和BCI竞赛Ⅲ中的data set Ⅳa)上进行验证,分别得到了81%和87%的平均分类精度。此外,与其他3种常用导联选择方法相比,MCFS算法获得了最高的平均分类精度,性能优越,为基于运动想象的BCI系统的应用提供了技术参考。
Aiming at the shortcoming that there exist too many channels in motor imagery(MI)-based braincomputer interface(BCI)systems,this paper proposes a channel selection algorithm based on multi-correlation forward searching(MCFS)algorithm such that the performance of BCI systems can be improved via the optimized the channel set.First,a forward searching algorithm is performed on the channel set via the training set.Meanwhile,the trust values of three correlation algorithms are updated with the classification accuracy of the validating set.Then,according to the above trust values,the high-quality channel set is selected,the common spatial pattern(CSP)algorithm is adopted to obtain the motor imagery related features,and the classification model is trained by means of support vector machine(SVM)with a linear kernel.Finally,the proposed algorithm is implemented on two datasets(BCI competition IV dataset I and BCI competition III datasets IVa),by which the average classification accuracy of 81%and 87%are achieved,respectively.Moreover,compared with three other common channel selection algorithms,the proposed MCFS algorithm obtains the highest average classification accuracy.These results show that the proposed MCFS algorithm has superior performance and provides a technical reference for the application of MI-based BCI system.