Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces
Sepehr Asgarian, Ze Wang, Feng Wan, Chi Man Wong, Feng Liu, Yalda Mohsenzadeh, Boyu Wang, Charles X. Ling
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Domain adaptation has gained significant attention to address the nonstationarity problem in electroencephalography (EEG) data in motor imagery (MI) classification. In MI classification, domain adaptation addresses cross-session variations, enhancing the classifier’s generalization capabilities. However, the existing methods have struggled to effectively capture both temporal and spatial features, resulting in limited classification accuracy. To tackle this issue, we propose the multiview adversarial contrastive network (MACNet). The proposed MACNet simultaneously learns spatial and temporal features in two different views: Euclidean and Riemannian. Furthermore, we introduce a multilevel domain mix-up technique to enhance domain alignment at both signal and embedding levels. The proposed MACNet method is evaluated on three public datasets. It achieves an accuracy of 83.79% on the BCI Competition IV dataset, 80.00% on the open source brain-machine interface (OpenBMI) dataset, and 85.83% on the sensorimotor rthythms (SMR) dataset that outperforms previous methods in cross-session transfer learning.