Cross-dataset motor imagery decoding — A transfer learning assisted graph convolutional network approach

The proliferation of portable electroencephalogram (EEG) recording devices has made it practically feasible to develop the motor imagery (MI) based brain–computer interfaces (BCIs). However, the low signal-to-noise ratio of EEG signals for abstract MI tasks, limited data, limited EEG channels, and strong inter- and intra-subject variability pose significant challenges for MI-task recognition. This paper proposes a transfer learning assisted graph convolutional network (GCN) modeling approach for cross-dataset MI decoding, one of the most challenging issues in this field. In the experiments, a multi-channel dataset with 62 electrodes and a few-channel dataset with 8 electrodes are utilized for cross-dataset modeling. To harness multi-channel information, we utilize the GCN module to aggregate topological features. The pre-trained model is guided with few-channel signals as inputs through a knowledge distillation framework. Subsequently, the pre-trained model is adapted to the few-channel dataset using a transfer learning strategy with minimal data training. Experiment results show that the proposed model achieves 3.92% and 3.83% more accuracy improvement compared with state-of-the-art models in the cross-validation and cross-session scenario respectively, demonstrating the effectiveness of the proposed approach in cross-dataset MI-EEG decoding, thus enabling more effective MI-BCI applications.