Thehybrid brain computer interface (BCI) based onmotor imagery (MI) and P300 has been a preferred strategy aiming to improve\nthe detection performance through combining the features of each. However, current methods used for combining these two\nmodalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to\noptimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a\ndual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can\nbe learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300\nare provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an\nevidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.
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