上海交通大学学报(自然版) ›› 2017, Vol. 51 ›› Issue (12): 1456-1463.doi: 10.16183/j.cnki.jsjtu.2017.12.008

• 学报(中文) • 上一篇    下一篇

基于功能性近红外光谱技术的脑机接口

焦学军,张朕,姜劲,王春慧,杨涵钧,徐凤刚,曹勇,傅嘉豪   

  1. 中国航天员科研训练中心人因工程国家重点实验室, 北京 100094
  • 出版日期:2017-11-30 发布日期:2017-11-30
  • 基金资助:
    国家自然科学基金项目(81671861),中国航天医学工程预先研究项目(YJGF151204) ,中国航天员科研训练中心人因国家重点实验室自主课题(SYFD150051805)

The Brain-Computer Interface Using Functional Near-Infrared Spectroscopy

JIAO Xuejun,ZHANG Zhen,JIANG Jin,WANG Chunhui,YANG Hanjun,XU Fenggang,CAO Yong,FU Jiahao   

  1. National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China
  • Online:2017-11-30 Published:2017-11-30

摘要: 为了探究功能性近红外光谱技术(fNIRS)对相同动作的运动想象和运动执行区分可行性以及前额皮层对运动想象和运动执行分类准确率的影响,研究测量了15位受试者手臂伸展和手指敲击的运动想象过程和运动执行过程的前额皮层和运动功能皮层的血氧变化信号.提取均值,斜率,二次项系数和近似熵特征建立基于支持向量机的四分类模型.对应于手臂伸展和手指敲击的四分类模型,分别实现了87.65%和87.58%的分类准确率.相对于单独运动功能皮层区域建立的运动功能皮层-fNIRS-脑机接口,引入前额皮层血氧变化信息能显著提高脑机接口分类性能,且对手指敲击动作的提高效果大于手臂伸展动作.因此,前额皮层区域的血氧响应生理特征能提高fNIRS-脑机接口的分辨性能,同时验证了fNIRS-脑机接口应用于多种肢体动作脑功能活动提取的可行性.

关键词: 功能性近红外光谱技术, 脑机接口, 运动想象, 运动执行, 支持向量机

Abstract: This paper explored the feasibility of recognizing motor imagery (MI) and motor execution (ME) in the same motion, as well as the affection of prefrontal cortex on the classification accuracy of MI and ME. We measured changes of oxygenated hemoglobin HbO2 and deoxygenated hemoglobinon (Hb) on prefrontal cortex (PFC) and motor cortex (MC) when 15 subjects performed hand extension and finger tapping tasks. Then mean, slope, quadratic coefficient and approximate entropy features were extracted from HbO2 as the input of support vector machine. With the four-class classifiers of brain-computer interface using functional near-infrared (fNIRS-BCI spectroscopy), 87.65% and 87.58% classification accuracy were realized corresponding to hand extension and finger tapping tasks. The classification accuracy increased significantly after adding PFC fNIRS signal, and greater increase emerged in finger napping than hand extension. In conclusion, it is effective for fNIRS-BCI to recognize MI and ME in the same motion, and the PFC region is sensitive in the four-class fNIRS-BCI classifiers.

Key words: functional near-infrared spectroscopy(fNIRS), brain-computer interface(BCI), motor imagery(MI), motor execution (ME), support vector machine (SVM)

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