mulEEG: A Multi-View Representation Learning on EEG Signals
This work addresses the problem of poor performance in EEG signal analysis for sleep-staging, offering a method that could improve diagnostic tools in healthcare, though it appears incremental as it builds on existing multi-view and self-supervised approaches.
The paper tackled the challenge of learning effective multi-view representations for EEG signals in sleep-staging tasks, proposing mulEEG, a novel multi-view self-supervised method that outperforms supervised training and multi-view baselines in transfer learning experiments.
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.