The Investigation of Brain-computer Interface for Motor Imagery and Execution Using Functional Near-infrared Spectroscopy

被引：1

作者：

Zhang, Zhen ^{[1
]}

Jiao, Xuejun ^{[1
]}

Xu, Fengang ^{[1
]}

Jiang, Jin ^{[1
]}

Yang, Hanjun ^{[1
]}

Cao, Yong ^{[1
]}

Fu, Jiahao ^{[1
]}

机构：

[1] China Astronaut Res & Training Ctr, Natl Key Lab Human Factors Engn, Beijing 100094, Peoples R China

来源：

INTERNATIONAL CONFERENCE ON INNOVATIVE OPTICAL HEALTH SCIENCE | 2017年 / 0245卷

基金：

中国国家自然科学基金;

关键词：

functional near-infrared spectroscopy; brain-computer interface; motor imagery; motor execution; support vector machine; FNIRS; EEG; BCI; CORTEX;

D O I：

10.1117/12.2267793

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Functional near-infrared spectroscopy (fNIRS), which can measure cortex hemoglobin activity, has been widely adopted in brain-computer interface (BCI). To explore the feasibility of recognizing motor imagery (MI) and motor execution (ME) in the same motion. We measured changes of oxygenated hemoglobin (HBO) and deoxygenated hemoglobin (HBR) on PFC and Motor Cortex (MC) when 15 subjects performing hand extension and finger tapping tasks. The mean, slope, quadratic coefficient and approximate entropy features were extracted from HBO as the input of support vector machine (SVM). For the four-class fNIRS-BCI classifiers, we realized 87.65% and 87.58% classification accuracy corresponding to hand extension and finger tapping tasks. In conclusion, it is effective for fNIRS-BCI to recognize MI and ME in the same motion.

引用

页数：6

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