用自监督图模型，把大量无标签脑电数据知识迁移到小样本有标签数据上
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
- 统一融合对比学习与掩码自编码的图预训练方法
- 在4个任务中实现优于基线的分类性能，最高提升6.2%
- 适合低密度脑电数据少样本场景下的模型迁移
如何有效利用大量无标签高密度脑电数据，提升小样本低密度脑电数据上的性能，是一个重要挑战。本文将其建模为图迁移学习与知识蒸馏问题，提出统一预训练图对比掩码自编码器蒸馏器EEG-DisGCMAE，弥合无标签与有标签、高密度与低密度脑电数据间的差距。方法引入新型统一图自监督预训练范式，无缝结合图对比预训练与图掩码自编码预训练。同时提出图拓扑蒸馏损失函数，使轻量级学生模型在预训练和微调阶段均可从高密度数据训练的教师模型中学习，通过对比蒸馏有效处理缺失电极问题。在两个临床脑电数据集的四个分类任务中验证了有效性，显著优于现有方法。
原文摘要 · Abstract (English)
Effectively utilizing extensive unlabeled high-density EEG data to improve performance in scenarios with limited labeled low-density EEG data presents a significant challenge. In this paper, we address this challenge by formulating it as a graph transfer learning and knowledge distillation problem. We propose a Unified Pre-trained Graph Contrastive Masked Autoencoder Distiller, named EEG-DisGCMAE, to bridge the gap between unlabeled and labeled as well as high- and low-density EEG data. Our approach introduces a novel unified graph self-supervised pre-training paradigm, which seamlessly integrates the graph contrastive pre-training with the graph masked autoencoder pre-training. Furthermore, we propose a graph topology distillation loss function, allowing a lightweight student model trained on low-density data to learn from a teacher model trained on high-density data during pre-training and fine-tuning. This method effectively handles missing electrodes through contrastive distillation. We validate the effectiveness of EEG-DisGCMAE across four classification tasks using two clinical EEG datasets with abundant data. The source code is available at https://github.com/weixinxu666/EEG_DisGCMAE.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。