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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,...
Common spatial pattern (CSP) algorithm achieves great success in two-class motor imagery based brain-computer interfaces (BCIs). Low information transfer rate is an intrinsic drawback of binary BCIs that limits their practical application. This paper generalizes the CSP algorithm from two-class to multi-class using a least-squares approach. The multi-class CSP algorithm is implemented by approximate...
Common spatial pattern (CSP) algorithm is a highly successful method for the motor imagery based brain-computer interfaces (BCIs) in the case of two task conditions. But low information transfer rate (ITR) is an intrinsic problem that binary BCIs face, and restricts their practical application. The most effective method to increase ITR is to extend two mental tasks to multiple tasks. This paper generalizes...
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