emadeldeen24/ADAST
[IEEE TETCI] "ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training"
This project helps sleep researchers and clinicians analyze raw brainwave (EEG) data from multiple patient groups or devices to automatically identify sleep stages. It takes in raw EEG recordings, potentially from different studies or labs, and outputs an accurate, automatic classification of sleep stages (e.g., Awake, REM, N1, N2, N3) for each patient, even when data sources vary. This is designed for researchers studying sleep disorders or developing diagnostic tools.
No commits in the last 6 months.
Use this if you need to consistently and accurately stage sleep across diverse EEG datasets collected from different studies or equipment.
Not ideal if you are a patient looking for a diagnostic tool or if you only work with a single, highly standardized EEG dataset.
Stars
39
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 06, 2023
Commits (30d)
0
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