Conditional adversarial domain adaptation based on sample weight

Abstract

To address the problem that some source domain samples that are difficult to transfer disturb the target domain data distribution due to the difference in transfer value between different motor imagery electroencephalogram (MI-EEG) sample data, and that the model has poor feature extraction and classification performance when adapting to different motor imagery datasets, this paper improves the conditional domain adversarial network (CDAN) method introduced by domain generalization technology, and proposes a conditional domain adaptation network based on sample weight (SW-CDAN) method. This method makes the entropy output by the domain discriminator as the sample weight, which is used to adjust the classification loss during the model training process, so that the model can extract transferable features from the common features of the data, thereby enhancing the model's category prediction ability and model generalization ability. The experimental results show that the SW-CDAN method can effectively improve the classification performance and model generalization ability of motor imagery EEG signals, so that even when facing a small amount of motor imagery EEG signals with low effective components, it can still maintain a high classification accuracy. The SWCDAN method achieves relatively high classification accuracy on BCI Competition IV 2a dataset, which is about 1.87% higher than CDAN method respectively.