Weighted Conditional Distribution Adaptation for Motor Imagery Classification.

Individual differences of electroencephalogram (EEG) signals can increase calibration difficulty, which is a major challenge in the practical application of brain computer interface (BCI). Transfer learning is an available method to predict the target subject’s EEG signals by learning an effective model from other subjects’ signals. This paper proposes a weight conditional distribution adaptation (WCDA) method, which can enhance feature transferability and discriminability by minimizing the conditional distribution of the same class between domains while maximizing the conditional distribution of different classes between domains. Moreover, a transferable source sample selection (TSSS) method is proposed to improve the transfer learning performance and reduce the computational cost. Experiments on two public motor imagery (MI) datasets demonstrated our approach outperforms the state of the art methods, thus providing an available way to reduce calibration effort for BCI applications.