Prototypical Contrastive Domain Adaptation Network for Nonstationary EEG Classification

Abstract

The identification of electroencephalography (EEG) signals’ cross sessions and subjects remains challenging due to the variability of data caused by extraneous factors and individual differences in EEG signals. Existing domain-adaptive transfer methods using cross-domain labeled samples for classification are too coarse and could lead to negative transfer problems. To solve this problem, we propose a prototypical contrastive domain adaptation (PCDA) network in this article. First, we align the data from different domains to reduce the data distribution differences for supporting the subsequent model construction. Then, a conditional domain adversarial network is used in the feature extraction stage to achieve domain alignment and learn deep feature representations. Second, we propose a scoring method to equivalently quantify the similarity of data from different domains using resting-state data and select similar source domain data to fine-tune the model. Finally, we propose a prototypical contrastive (PC) learning module. In-domain PC learning captures and compares the category-wise semantic structure of the data and the learned representations to enable the clustering of similar features. Cross-domain PC learning encodes and compares the semantic structure in shared embedding space to enable self-supervised feature alignment and reduce negative transfer. The experimental results show that the PCDA network achieves better results on the datasets of brain-computer interface (BCI) Competition IV II-a and II-b, and the ablation experiments validate the efficacy of the method.