Unsupervised multi-source domain adaptation via contrastive learning for EEG classification

Chengjian Xu, Yonghao Song, Qingqing Zheng, Qiong Wang, Pheng-Ann Heng

Abstract: Highlights•We propose a novel unsupervised multi-source domain adaptation framework to effectively learn subject-invariant representations for EEG-based motor imagery.•We utilize contrastive learning to address each source-target and inter-source variability in the multi-source domain adaptation process, facilitating learning subject-independent representations.•We have validated the proposed method on four motor imagery datasets. The experimental results demonstrate the superior performance of our method.