In those EEG-based classification algorithms, SVM based on structural risk minimization yields good performances in many applications, especially for solving problems with high dimension, nonlinearity, and small dataset. However, it is often unclear what the most suitable kernel in SVM is, and so the user may wish to combine several possible kernels. Multiple kernel learning SVM (MKL-SVM) is an efficient way of optimizing kernel weights [22, 23]. Compared with one single kernel SVM, MKL-SVM can enhance the interpretability of the decision function and improve the performance [24, 25].
