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datasets:
- nm000185
---
# plot_sleep_staging_usleep
Pretrained artifacts for the Braindecode tutorial `examples/applied_examples/plot_sleep_staging_usleep.py`.
These files are meant to be loaded by the tutorial so the docs can show stable predictions without retraining the model from scratch.
## Stored files
- `params.safetensors`: classifier parameters
- `history.json`: Skorch training history used by the tutorial plots
- `metadata.json`: summary metrics for the stored checkpoint
<!-- braindecode:training-data -->
## Training data
The data that shaped these weights. The ids are the `datasets:` field of this card,
which braindecode reads into `model.card_data.datasets` when you call
`from_pretrained` (MOABB class names for MOABB datasets, OpenNeuro / NEMAR
accessions, Hub ids otherwise). `nm`/`on` ids are datasets on [NEMAR](https://nemar.org)
(`on` = NEMAR's copy of the OpenNeuro dataset with the same number). Rows marked
`private:` are data the authors did not release; they are not in `datasets:`.
| `datasets:` id | Dataset | Stage | Modality |
|---|---|---|---|
| [`nm000185`](https://nemar.org/dataset/nm000185) | [Sleep-EDF Database Expanded, Sleep Cassette study (PhysioNet sleep-edfx 1.0.0), loaded with braindecode SleepPhysionet (EEG channels Fpz-Cz / Pz-Oz only)](https://physionet.org/content/sleep-edfx/1.0.0/) | training | EEG |
- Supervised USleep (5 classes). SleepPhysionet subject_ids=[0, 1], recording_ids=[2], crop_wake_mins=30, then crop=(0, 12000 s) (first 400 30-s windows) -> subject 0 night 2 trains, subject 1 night 2 is the validation recording for EarlyStopping(load_best=True). Split is BaseConcatDataset.split(dict(train=..., valid=...)), whose values are RECORDING INDICES into the concatenated dataset, not subject ids (braindecode/datasets/base.py split: `[self.datasets[ds_ind] for ds_ind in ds_inds]`); SleepPhysionet orders recordings subject by subject, night 1 before night 2 (mne fetch_data loops `for subject in subjects`).
- Sources: braindecode git 05e606dd^:scripts/train_and_push_tutorial_checkpoints.py (offline trainer that produced the tutorial checkpoints; removed from the repo in 05e606dd, 2026-04-19, and moved to https://gist.github.com/bru...; W/bd/examples/applied_examples/plot_sleep_staging_usleep.py; https://huggingface.co/braindecode/plot_sleep_staging_usleep/resolve/main/metadata.json.
## Cite Braindecode
If you use this braindecode port in scientific work, cite the software on Zenodo,
[doi:10.5281/zenodo.17699192](https://doi.org/10.5281/zenodo.17699192),
and the original model and datasets cited above.
```bibtex
@software{braindecode,
author = {Aristimunha, Bruno and
Guetschel, Pierre and
Wimpff, Martin and
Gemein, Lukas and
Rommel, Cedric and
Banville, Hubert and
Sliwowski, Maciej and
Wilson, Daniel and
Brandt, Simon and
Gnassounou, Théo and
Paillard, Joseph and
{Junqueira Lopes}, Bruna and
Sedlar, Sara and
Moreau, Thomas and
Chevallier, Sylvain and
Gramfort, Alexandre and
Schirrmeister, Robin Tibor},
title = {Braindecode: toolbox for decoding raw electrophysiological brain data
with deep learning models},
url = {https://github.com/braindecode/braindecode},
doi = {10.5281/zenodo.17699192},
publisher = {Zenodo},
license = {BSD-3-Clause},
}
```
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