emadeldeen24/eval_ssl_ssc
[TNSRE 2023] Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation
This project helps sleep researchers and clinicians analyze large amounts of raw brainwave data (EEG) from sleep studies to automatically identify sleep stages. It takes unlabeled EEG data and a small portion of labeled data, then outputs accurate sleep stage classifications, allowing for efficient analysis even when expert annotations are scarce. It's designed for professionals working with polysomnography in research labs or clinical settings.
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Use this if you need to classify sleep stages from large datasets of EEG recordings but have limited expert-annotated examples.
Not ideal if you already have fully labeled datasets or are not working with sleep stage classification from EEG.
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47
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7
Language
Python
License
MIT
Category
Last pushed
Sep 19, 2023
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