EEG-DisGCMAE combines graph contrastive and masked autoencoder pre-training with a graph topology distillation loss to improve low-density EEG classification using high-density and unlabeled data.
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Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
EEG-DisGCMAE combines graph contrastive and masked autoencoder pre-training with a graph topology distillation loss to improve low-density EEG classification using high-density and unlabeled data.