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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 complete stager (180 tensors: tokenizer, channel pooling, temporal Transformer and staging head), 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 encoder (channel-agnostic tokenizer,
channel pooling and temporal Transformer) lives in `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. It
holds the whole encoder except its trial-level temporal pooling, which sleep staging does
not use. 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. The release encodes BAS, RESP, EKG and EMG
# channels as separate modalities; name the modality of each channel.
model = SleepFMStager.from_pretrained(
n_chans=7,
n_outputs=5,
n_times=38400,
sfreq=128,
channel_modalities=["BAS"] * 3 + ["RESP"] * 2 + ["EKG", "EMG"],
)
model.eval()
```
`config.json` leaves `channel_modalities` unset (`null`), because it depends on the
montage. Without it every channel is encoded as a single modality and `from_pretrained`
warns.
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.
## Revisions
- [`8681fba`](https://huggingface.co/braindecode/SleepFMStager/tree/8681fbade497e2ce103e1b68b01fbb3d56e809f2):
tokenizer and staging head only (93 tensors). `SleepFMStager.from_pretrained` reads the
encoder's channel pooling and temporal Transformer from
[`braindecode/SleepFM`](https://huggingface.co/braindecode/SleepFM) at load time.
- Current revision: the complete stager (180 tensors), bit-identical to the upstream
`model_base/best.pt` + `model_sleep_staging/best.pth`, re-exported with the fixed port of
braindecode PR #1106. `config.json` now lists every constructor argument; the defaults
(`n_chans=4`, `n_times=3840`, `sfreq=128`) are unchanged. Its outputs are bit-identical to
those of `8681fba` completed from `braindecode/SleepFM`, and both revisions load with
`SleepFMStager.from_pretrained` (pass `revision=` to pin one).
## 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.
|