likith012/mulEEG
Official implementation of our MICCAI 2022 paper "mulEEG: A Multi-View Representation Learning on EEG Signals"
This project helps researchers and clinicians analyze raw Electroencephalogram (EEG) signals to identify sleep stages. It takes unlabeled EEG data as input and produces high-quality, 'learned' representations of these signals, which can then be used for tasks like automated sleep staging, even outperforming traditional supervised methods. Neuroscientists, sleep researchers, and medical professionals working with EEG data would find this valuable.
Use this if you need to extract meaningful insights from large amounts of unlabeled EEG data, particularly for sleep analysis, and want a method that can learn effective representations without needing extensive manual labeling.
Not ideal if you are looking for a simple, off-the-shelf diagnostic tool that provides direct medical interpretations without requiring further integration or understanding of machine learning models.
Stars
36
Forks
23
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 29, 2025
Commits (30d)
0
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