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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)
Department机器人学研究室
Conference Name7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Conference DateJuly 31 - August 4, 2017
Conference PlaceHawaii, USA
Author of SourceIEEE Robotics and Automation Society
Source Publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages484-489
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183905873714
WOS IDWOS:000447628700089
Contribution Rank1
ISBN978-1-5386-0489-2
KeywordBrain Computer Interface Common Spatial Pattern Training Time Multi-subject Model
Abstract

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.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21342
Collection机器人学研究室
Corresponding AuthorZou YJ(邹宜君)
AffiliationShenyang Institute of Automation (SIA) ,Chinese Academy of Sciences ,Shenyang, China
Recommended Citation
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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