An end-to-end multi-stem deep decomposition network built in PyTorch to isolate and eliminate EOG, EMG, and ECG artifacts from real-world clinical EEG recordings while fully preserving underlying neural frequencies.
- Core Framework: Physics-locked additive identity constraint safely structures artifact extraction without signal hallucination.
- Domain Training: Sequential Knowledge-Stacking across clinical and emotional benchmarks.
- License Protection: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
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