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An Approach to Minimize Calibration Time for Brain Computer Interface Based on Motion Imagery
Zou YJ(邹宜君); Zhao XG(赵新刚); Xu WL(徐卫良); Han JD(韩建达)
作者部门机器人学研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码484-489
收录类别EI ; CPCI(ISTP)
EI收录号20183905873714
WOS记录号WOS:000447628700089
产权排序1
ISBN号978-1-5386-0489-2
关键词Brain Computer Interface Common Spatial Pattern Training Time Multi-subject Model
摘要

The long training time is a big problem that block the application of brain computer interface. This paper solve this problem using the existing dataset of many subjects. By analyzing the differences in EEG signal among different users, removing these differences as much as possible and exploits the common points of different individual, the proposed method can correct the data in existing dataset to a new dataset close to the target data. Then using the ensemble method, we combine the model of the existing dataset to a final model. The result shows that the proposed method can decrease the training time greatly while the recognize accuracy can also meet the need.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21342
专题机器人学研究室
通讯作者Zou YJ(邹宜君)
作者单位Shenyang Institute of Automation (SIA) ,Chinese Academy of Sciences ,Shenyang, China
推荐引用方式
GB/T 7714
Zou YJ,Zhao XG,Xu WL,et al. An Approach to Minimize Calibration Time for Brain Computer Interface Based on Motion Imagery[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:484-489.
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