A deep transfer learning network with two classifiers based on sample selection for motor imagery brain-computer interface

The non-stationary of Motor Imagery (MI) electroencephalogram (EEG) signals makes the traditional machine learning methods ineffective in EEG recognition across time, which limits the practicability of Motor Imagery Brain-computer Interface(MI BCI). Transfer learning makes the classification model of non-stationary MI EEG signals collected and trained at different times universal. In this paper, we proposed a new deep transfer neural network model that can effectively utilize labeled EEG data from previous times, thereby achieving good recognition performance for a small number of labeled EEG signals at the current time. Firstly, a filter bank was used to filter the original motor imagery EEG, and the filtered EEG retained the key features of each domain through the domain specific attention module. Then designed two domain adaptation modules simultaneously. The first domain adaptation module selected similar source domain data, and the second domain adaptation module minimized the difference between the source and target domains. At last, two adversarial classifiers were used to improve the accuracy and robustness of the classification. The results of average classification accuracy, feature visualization, and paired t-test on two public datasets all indicated that the classification performance of the proposed method was significantly higher than other comparison methods. The experimental results on two public datasets showed that the proposed method performed better than the comparison methods when classifying only a small number of labeled MI EEG data, and can achieve higher classification accuracy. This method has practical application value in the rehabilitation of stroke patients.