Generalizable Movement Intention Recognition with Multiple Heterogeneous EEG Datasets.

Human movement intention recognition is important for human-robot interaction. Existing work based on motor imagery electroencephalogram (EEG) provides a non-invasive and portable solution for intention detection. However, the data-driven methods may suffer from the limited scale and diversity of the training datasets, which result in poor generalization performance on new test subjects. It is practically difficult to directly aggregate data from multiple datasets for training, since they often employ different channels and collected data suffers from significant domain shifts caused by different devices, experiment setup, etc. On the other hand, the inter-subject heterogeneity is also substantial due to individual differences in EEG representations. In this work, we developed two networks to learn from both the shared and the complete channels across datasets, handling inter-subject and inter-dataset heterogeneity respectively. Based on both networks, we further developed an online knowledge co-distillation framework to collaboratively learn from both networks, achieving coherent performance boosts. Experimental results have shown that our proposed method can effectively aggregate knowledge from multiple datasets, demonstrating better generalization in the context of cross-subject validation.