Article(id=1242135695972705264, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242135676540494779, articleNumber=null, orderNo=13, doi=10.3981/j.issn.1000-7857.2017.02.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1475164800000, receivedDateStr=2016-09-30, revisedDate=1481040000000, revisedDateStr=2016-12-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1487227671623, onlineDateStr=2017-02-16, pubDate=1485532800000, pubDateStr=2017-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1487227671623, onlineIssueDateStr=2017-02-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774077827682, creator=sys-migrate, updateTime=1774077827682, updator=sys-migrate, issue=Issue{id=1242135676540494779, tenantId=1146029695717560320, journalId=1146031591421210625, year='2017', volume='35', issue='2', pageStart='1', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='1485532800000', pubDateStr='2017-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774077823049, creator='sys-migrate', updateTime=1774077823049, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=60, endPage=64, ext={EN=ArticleExt(id=1242135699714023441, articleId=1242135695972705264, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Enhancement of brain-computer interface using functional nearinfrared spectroscopy based on correlation index analysis, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=Due to advantages such as robustness with respect to motion artifact, suitability for special populations like infants, and being able to be measured in wearable settings, the functional near-infrared spectroscopy (fNIRS) is an emerging and more and more important brain functional imaging modality in many research fields e.g. the brain computer interface, the psychology and the cognitive science. Motor imagination is an important paradigm in the rehabilitation trainings for disabled people. With the development of wearable fNIRS systems, these systems may assist the disabled people in long-term brain rehabilitation trainings at home or in community. However, the classification accuracies of the current fNIRS-based motor imaginary tasks are generally low. This paper aims to improve the classification accuracy of the fNIRS-based motor imaginary task by the individualized parameter optimization using the Pearson correlation based R2 method. In this experiment, the concentration variation data of hemoglobin species during the left and right hand motor imaginary tasks of 17 subjects were collected using the fNIRS method, and the support vector machine (SVM) classifier was then adopted for classification. Experimental results show that the classification accuracy is significantly improved by the parameter optimization using the R2 method. With the R2 method, the percentage of the subjects with classification accuracies above 60% is turned from 58.8% to 94% and that with classification accuracies above 65% is turned from 41.2% to 64.7% in the whole subject pool., authors=LI Zhe1 , ZHANG Shen1 , ZHENG Yanchun1 , WANG Daifa1 , MA Jian'ai1 , WANG Ling1 , LI Deyu1,2 , authorsList=LI Zhe, ZHANG Shen, ZHENG Yanchun, WANG Daifa, MA Jian'ai, WANG Ling, LI Deyu, authorCompany=1. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;Pearson 积差相关系数的相关指数R 2 ，对被试进行个性化参数优化，期望改善运动想象的分类结果。实验采集了17名被试的左、右手运动想象任务期间大脑皮层主运动区的血红蛋白浓度变化数据，并采用支持向量机（SVM）分类。结果表明，经过R 2 参数优化之后，分类准确率相对无优化情况显著提升，分类准确率在60%以上的被试比例由原本的58.8%提高到了94%，分类准确率在65%以上的被试比例由原本的41.2%提升到了64.7%。, authors=李喆1 , 张屾1 , 郑燕春1 , 汪待发1 , 马建爱1 , 王玲1 , 李德玉1,2 , authorsList=李喆, 张屾, 郑燕春, 汪待发, 马建爱, 王玲, 李德玉, authorCompany=1. 北京航空航天大学生物与医学工程学院, 北京 100191;
科技导报
| 研究论文 2017, 35(2): 60-64
基于相关指数分析增强的功能近红外光谱脑机接口
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李喆1 , 张屾1 , 郑燕春1 , 汪待发1 , 马建爱1 , 王玲1 , 李德玉1,2
作者信息
1. 北京航空航天大学生物与医学工程学院, 北京 100191;
通讯作者:
王玲(通信作者),讲师,研究方向为生物医学建模和信息处理,电子信箱:lingwang@buaa.edu.cn;李德玉(共同通信作者),教授,研究方向为生物医学建模、图像和信息处理,电子信箱:deyuli@buaa.edu.cn
Enhancement of brain-computer interface using functional nearinfrared spectroscopy based on correlation index analysis
Affiliations
出版时间: 2017-01-28
doi: 10.3981/j.issn.1000-7857.2017.02.008
文章导航
由于对运动伪迹不敏感、适合特殊人群和可穿戴式检测等优势，功能近红外光谱技术（fNIRS）在脑机接口（BCI）、心理认知等领域发挥着日益重要的作用。肢体运动想象是BCI在残疾人康复训练等领域应用的重要范式，伴随穿戴式fNIRS的发展，有望帮助残疾人在家庭或社区开展长期脑康复训练。本文针对目前基于fNIRS的运动想象任务分类准确率普遍不高这一现状，应用基于Pearson 积差相关系数的相关指数R 2 ，对被试进行个性化参数优化，期望改善运动想象的分类结果。实验采集了17名被试的左、右手运动想象任务期间大脑皮层主运动区的血红蛋白浓度变化数据，并采用支持向量机（SVM）分类。结果表明，经过R 2 参数优化之后，分类准确率相对无优化情况显著提升，分类准确率在60%以上的被试比例由原本的58.8%提高到了94%，分类准确率在65%以上的被试比例由原本的41.2%提升到了64.7%。
功能近红外脑功能成像
/
运动想象
/
支持向量机
/
相关指数分析
Due to advantages such as robustness with respect to motion artifact, suitability for special populations like infants, and being able to be measured in wearable settings, the functional near-infrared spectroscopy (fNIRS) is an emerging and more and more important brain functional imaging modality in many research fields e.g. the brain computer interface, the psychology and the cognitive science. Motor imagination is an important paradigm in the rehabilitation trainings for disabled people. With the development of wearable fNIRS systems, these systems may assist the disabled people in long-term brain rehabilitation trainings at home or in community. However, the classification accuracies of the current fNIRS-based motor imaginary tasks are generally low. This paper aims to improve the classification accuracy of the fNIRS-based motor imaginary task by the individualized parameter optimization using the Pearson correlation based R2 method. In this experiment, the concentration variation data of hemoglobin species during the left and right hand motor imaginary tasks of 17 subjects were collected using the fNIRS method, and the support vector machine (SVM) classifier was then adopted for classification. Experimental results show that the classification accuracy is significantly improved by the parameter optimization using the R2 method. With the R2 method, the percentage of the subjects with classification accuracies above 60% is turned from 58.8% to 94% and that with classification accuracies above 65% is turned from 41.2% to 64.7% in the whole subject pool.
functional near-infrared spectroscopy
/
motor imagery
/
support vector machine
/
correlation index analysis
李喆, 张屾, 郑燕春, 汪待发, 马建爱, 王玲, 李德玉.
基于相关指数分析增强的功能近红外光谱脑机接口.
科技导报,
2017
, 35
(2)
: 60
-64
.
DOI: 10.3981/j.issn.1000-7857.2017.02.008
LI Zhe, ZHANG Shen, ZHENG Yanchun, WANG Daifa, MA Jian'ai, WANG Ling, LI Deyu.
Enhancement of brain-computer interface using functional nearinfrared spectroscopy based on correlation index analysis[J].
Science & Technology Review ,
2017
, 35
(2)
: 60
-64
.
DOI: 10.3981/j.issn.1000-7857.2017.02.008
2017年第35卷第2期
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文章信息
doi: 10.3981/j.issn.1000-7857.2017.02.008
接收时间：2016-09-30
首发时间：2017-02-16
出版时间：2017-01-28
收稿日期：2016-09-30
修回日期：2016-12-07
通讯作者:
王玲(通信作者),讲师,研究方向为生物医学建模和信息处理,电子信箱:lingwang@buaa.edu.cn;李德玉(共同通信作者),教授,研究方向为生物医学建模、图像和信息处理,电子信箱:deyuli@buaa.edu.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2017.02.008
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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