An Unweighted Exhaustive Diagonalization Based Multi-Class Common Spatial Pattern Algorithm in Brain-Computer Interfaces

In binary brain-computer interfaces (BCI) based on motor imagery, common spatial pattern (CSP) successfully discriminates two-class EEG data. However, low information transfer rate is an intrinsic drawback of binary BCIs that limits their practical applications. It's essential to extend binary...

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Bibliographic Details
Published in2010 2nd International Conference on Information Engineering and Computer Science pp. 1 - 5
Main Authors Kui Chen, Qingguo Wei, Yuhui Ma
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Accuracy
Algorithm design and analysis
Approximation algorithms
Classification algorithms
Covariance matrix
Electroencephalography
Joints
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Summary:In binary brain-computer interfaces (BCI) based on motor imagery, common spatial pattern (CSP) successfully discriminates two-class EEG data. However, low information transfer rate is an intrinsic drawback of binary BCIs that limits their practical applications. It's essential to extend binary CSP algorithm to multi-class paradigms. In this paper, a new approximate joint diagonalization (AJD) method, named unweighted exhaustive diagonalization with Gauss iterations (UEDGI) is proposed for the extension. The UEDGI based multi-class CSP algorithm is applied to five data sets recorded during motor imagery of left hand, right hand, foot or tongue. The performance of the algorithm is accessed by classification accuracy and convergence speed, and compared with other two multi-class CSP algorithms, one versus one (OVO) and one versus the rest (OVR). Experimental results show that the UEDGI based multi-class CSP performs best in both classification rate and running speed.
ISBN:1424479398
9781424479399
ISSN:2156-7379
DOI:10.1109/ICIECS.2010.5677859