<ul data-eligibleForWebStory="false"><li>Researchers have introduced a Unified Pre-trained Graph Contrastive Masked Autoencoder Distiller, EEG-DisGCMAE, to enhance performance by leveraging unlabeled high-density EEG data to aid limited labeled low-density EEG data.</li><li>The approach integrates graph contrastive pre-training with graph masked autoencoder pre-training and introduces a graph topology distillation loss function to facilitate knowledge transfer from teacher models trained on high-density data to lightweight student models trained on low-density data.</li><li>The method effectively addresses missing electrodes through contrastive distillation, and it has been validated across four classification tasks using clinical EEG datasets.</li><li>The research paper and source code can be accessed at arXiv:2411.19230v2 and https://github.com/weixinxu666/EEG_DisGCMAE, respectively.</li></ul>

Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG

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