A hybrid EEG-based BCI for robot grasp controlling

被引：0

作者：

Zhang, Wenchang ^{[1
,2
]}

Sun, Fuchun ^{[1
]}

Liu, Chunfang ^{[1
]}

Su, Weihua ^{[2
]}

Tan, Chuanqi ^{[1
]}

Liu, Shaobo ^{[1
]}

机构：

[1] Tsinghua Univ, State Key Lab Intelligent Technol & Syst, Comp Sci & Technol Sch, FIT Bldg, Beijing 100084, Peoples R China

[2] Inst Med Equipment, Wandong Rd, Tianjin, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2017年

基金：

中国国家自然科学基金;

关键词：

BRAIN-COMPUTER-INTERFACE; 2-D CURSOR CONTROL; RHYTHM; OPERATION; IMAGERY; SYSTEM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Brain-Computer Interfaces (BCI) can help disable people to improve human - environment interaction and rehabilitation. Grasping objects with EEG-based BCI has become a popular and hard research in recent years due to the high degree of freedom robot and complex grasp planning. Unlike commonly used paradigms, we propose a pipeline of hybrid EEG-based BCI for robot grasping by shared control to solve the key problems including target object selection, robot intelligent planning and shared control by both user intension and robot. Six experimental users could successfully use the system to grasp a number of objects in a test scene. The results of grasping experiment demonstrate that our method achieve an effective performance.

引用

页码：3278 / 3283

页数：6

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