Motor Imagery Recognition Method Based on Multisource Transfer Learning and Multiclassifier Fusion

Abstract

There are two common problems in the field of motor imagery (MI) recognition, which are poor generalization and low recognition performance. A recognition method based on multisource transfer learning and multiclassifier fusion is therefore proposed to realize the MI classification. In this approach, multisource transfer learning method is used to transfer samples from multiple source domains to target domain. The source domain selection method based on distribution similarity is designed to select those source domains whose distribution is similar to the target domain, and samples with high information entropy are selected from these source domains for transferring. Then, an MI classification method is proposed through the fusion of multiple classifiers. The classifiers are trained by labeled samples in the target domain and the transferred samples in multiple source domains. The new sample in the target domain can be identified by the weight fusion of the results of these classifiers. In order to verify the effectiveness of the proposed method, four types of motor imagery in the BCI Competition IV dataset 2a were used to evaluate the recognition ability, and the results approved an excellent recognition and generalization performance as well as a better training efficiency comparing to the well-applied methods nowadays.