Paper Info
Title
An source-based common spatial filter selection for improving mis-triggering problem in brain-computer interface based on motor imagery
Abstract
Motor imagery based brain-computer interface (MI-BCI) has been proven to be effective in post-stroke rehabilitation. However, the low specificity of MI-BCI is usually associated with a mis-triggering problem, which means the irrelevant task would induce a false positive output. Traditional classification method collects more training data for interferences which overburdens patients. To reduce mis-triggering rate in MI-BCI, we proposed a source selection common spatial pattern (SS-CSP) method to build the MI classifier without extra training sessions on irrelevant tasks. Using this approach, we were able to acquire a mis-triggering rate for interference task as low as 36%, showing a decrease of nearly 23% compared to traditional method. Meanwhile, the accuracy of two methods only had difference of 6%. The results suggest that our method can significantly depress the mis-triggering rate and has potential effect on post-stroke rehabilitation.
Year
DOI
Venue
2017
10.1109/ICAwST.2017.8256474
2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)
Keywords
Field
DocType
stroke rehabilitation,eeg,source,mis-triggering problem
Common spatial pattern,Rehabilitation,Pattern recognition,Computer science,Brain–computer interface,Interference (wave propagation),Artificial intelligence,Classifier (linguistics),Electroencephalography,Spatial filter,Motor imagery
Conference
ISSN
ISBN
Citations 
2325-5986
978-1-5386-2966-6
0
PageRank 
References 
Authors
0.34
5
8
Name
Order
Citations
PageRank
Lichao Xu100.34
Jiapeng Xu262.46
Kun Wang322.07
Zhongpeng Wang404.06
Minpeng Xu52717.17
Feng He6169.45
Dong Ming710551.47
Hongzhi Qi84920.61