---
license: cc-by-nc-4.0
library_name: braindecode
tags:
  - eeg
  - polysomnography
  - sleep-staging
  - foundation-model
  - braindecode
---

# SleepFMStager — pretrained sleep stager

Mirror of the official **SleepFM** sleep-staging model, re-hosted for stable loading from
[Braindecode](https://github.com/braindecode/braindecode).

SleepFM is a multimodal polysomnography (PSG) foundation model introduced in:

> R. Thapa et al., *"A multimodal sleep foundation model for disease prediction,"*
> **Nature Medicine** (2026). https://doi.org/10.1038/s41591-025-04133-4

## Files

| File | Description |
|------|-------------|
| `model.safetensors` | The full stager, with the parameter names of `braindecode.models.SleepFMStager` |
| `config.json` | Architecture of the checkpoint, read by `from_pretrained()` |

Upstream ships the stager in two pieces: the channel-agnostic tokenizer lives in the
encoder checkpoint (`model_base/best.pt`) and the staging head in
`model_sleep_staging/best.pth`. This file **merges both**, so a single call returns a
model that is pretrained end to end, its five-class output layer included. The tensors are
those of the upstream artifacts; only the keys were rewritten to the library's parameter
names. Loading this file or the two upstream ones gives bit-identical outputs.

The upstream artifacts themselves are kept, byte-for-byte, in
[`braindecode/SleepFM`](https://huggingface.co/braindecode/SleepFM).

## Usage

```python
from braindecode.models import SleepFMStager

# Defaults to this repository.
model = SleepFMStager.from_pretrained(n_chans=4, n_outputs=5, n_times=3840, sfreq=128)
model.eval()
```

The output has shape `(batch, n_outputs, n_patches)`: one prediction per **5-second
patch**, not per 30-second scoring epoch, so six predictions cover one scored epoch. For
this checkpoint the five classes are Wake, N1, N2, N3 and REM. Input must be sampled at
**128 Hz**. Pass `n_outputs` different from 5 to reinitialise the output layer for another
label set.

## License & attribution

- **License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).**
- Copyright (c) 2025 Rahul Thapa.
- Upstream source: https://github.com/zou-group/sleepfm-clinical

These weights are **not** covered by Braindecode's BSD-3 license and inherit the
upstream **noncommercial** terms. Re-hosted for reproducibility and stable
availability only; attribution and the CC BY-NC 4.0 restriction are preserved.