Transfer learning algorithm design for feature transfer problem in motor imagery brain-computer interface

The non-stationary of the motor imagery electroencephalography(MI-EEG) signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI). The non-stationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates, which reduces the classification accuracy of MI-BCI. In this paper, we propose a Kullback-Leibler divergence (KL)-based transfer learning algorithm to solve the problem of feature transfer, the proposed algorithm uses KL to measure the similarity between the training set and the testing set, adds support vector machine (SVM) classification probability to classify and weight the covariance, and discards the poorly performing samples. The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms, especially for subjects with medium classification accuracy. Moreover, the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have, which is significant for the application of MI-BCI.