---
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},
}
```