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Motor imagery based brain-computer interface (BCI) translates subject's motor intention into a control signal through electroencephalogram (EEG) pattern classification. In this paper, a large margin nearest neighbor (LMNN) method is applied for the classification of multi-class BCI based on motor imagery. The main idea of LMNN is to learn a Mahalanobis distance that tries to collapse examples in the...
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