LAUSR

In this paper, we propose multiple kernel learning (MKL) based on three discriminant features to learn an efficient P300 classifier to improve the accuracy of character recognition in a P300 speller BCI. Three discriminant features are specified in raw samples and two morphological features, which can complement the MKL of a P300 classification. A linear kernel is established for each discriminant feature. A kernel weight differentiates the linear kernel to both explore complementary information among the three discriminant features and weigh a contribution of each discriminant feature for the MKL. Here, the 1 norm regularization of the kernel weight ultimately enforces an optimal discriminant feature set of the MKL of a P300 classification.The performance of the proposed method is then evaluated according to the size of the three discriminant feature sets that are generated from dataset II of BCI competition III. Compared to an existing SVM-based classification method, the proposed method consistently obtains better or similar accuracy in terms of character recognition, with a different execution time for the variable size of the three discriminant feature sets. Furthermore, the kernel weight of the raw samples was found to consistently be more dominant than the kernel weight of the two morphological features on the variable size of the three discriminant feature sets. This finding means that the two morphological features supplement the lack of the raw samples for the MKL of a P300 classification. We ultimately could conclude that the proposed method is sufficiently robust to improve the accuracy of character recognition with a different time for the variable size of the three discriminant feature sets in a P300 speller BCI.