Application of self-adaptive multiple-kernel extreme learning machine to improve MI-BCI performance of subjects with BCI illiteracy

•It is difficult to extract common and recognizable features from BCI illiterate subjects who do not exhibit typical brain activity. Inspired by the kernel trick, we proposed a multiple kernel extreme learning machine framework, which can map the input features to multi-dimensional nonlinear spaces as much as possible, so as to increase the separability probability of the features of BCI illiteracy.•We summarize the following two conclusions: the more kernels were used, the better the BCI performance is, especially for the BCI illiteracy; non-sparse multiple kernel learning can usually outperform the sparse form. As mentioned above we proposed the linear combination of four kernels in non-sparse form.•We employed differential evolution (DE) to find the optimal initial parameters of the classifier for the purpose of obtaining the optimal classifier suitable for the current system.•In comparison with the state-of-the-art classifiers, the experimental results showed that the performance of our method outperformed other control methods, especially for the BCI illiterate subjects.