论文标题
Muleeg：关于脑电图信号的多视图表示学习
mulEEG: A Multi-View Representation Learning on EEG Signals
论文作者
论文摘要
使用多个相互影响的多种视图对有效表示进行建模是具有挑战性的，并且现有方法在脑电图（EEG）信号上的性能较差，以进行睡眠阶段阶段任务。在本文中，我们提出了一种新颖的多视图自我监督方法（Muleeg），以进行无监督的脑电图表示学习。我们的方法试图有效利用多种视图中可用的补充信息来学习更好的表示。我们引入了多种损失，进一步鼓励了跨多种观点的互补信息。我们无法使用标签的方法击败了监督培训，同时在转移学习实验上超过了多视图基线方法，该方法在睡眠阶段阶段任务上进行了。我们认为我们的方法能够使用互补的多视图来学习更好的表示。
Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleep-staging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to labels beats the supervised training while outperforming multi-view baseline methods on transfer learning experiments carried out on sleep-staging tasks. We posit that our method was able to learn better representations by using complementary multi-views.
