Semi-supervised Multi-Source Transfer Learning for Motor Imagery Recognition

Abstract

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.