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.
In the field of motor imagery (MI) recognition, poor generalization and low recognition performance are major challenges. An MI recognition method based on semi-supervised learning and multi-source transfer learning is proposed. In this approach, samples are transferred from some source domains to the target domain using the multi-source transfer learning method. The source domains selection method based on distribution similarity is designed to select source domains with similar distribution to the target domain, and samples with high information entropy are selected from these source domains for transfer. In this regard, we propose a semi-supervised learning labeling method for labeling the unlabeled samples of the target domain, which utilizes the labeling information from a few labeled samples without increasing the labeling cost. The sample confidence measurement method and the dynamic adjustment mechanism are proposed to ensure labeling accuracy and minimize the influence of mislabeled samples. A fusion classification model can identify the new sample in the target domain. As a measure of the effectiveness of the proposed method, four types of MI from the BCI Competition IV dataset 2A were used to evaluate the recognition ability, and the outcomes confirmed an excellent recognition performance as well as a superior training efficiency when compared with the currently used methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.