SleepFM — pretrained encoder

Mirror of the official SleepFM encoder checkpoint, re-hosted for stable loading from 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

The downstream sleep stager lives in a separate repository, braindecode/SleepFMStager, because a Braindecode config.json describes exactly one architecture.

Files

File	Description
model.safetensors	The encoder, with the parameter names of braindecode.models.SleepFM
config.json	Architecture of the checkpoint, read by from_pretrained()
model_base/best.pt	The upstream artifact, byte-for-byte, kept for provenance
model_sleep_staging/best.pth	The upstream staging artifact, byte-for-byte (see SleepFMStager)

model.safetensors holds the same tensors as model_base/best.pt; only the keys were rewritten (the module. prefix of the distributed training run stripped, and positional_encoding.pe renamed) so that the library needs no remapping code at load time. Loading either way gives bit-identical outputs.

Note that the released encoder is contrastive and carries no classification head: final_layer is randomly initialised and must be fine-tuned.

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 (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
SSC	Stanford Sleep Clinic PSG cohort (Stanford Sleep Dataset)	pretraining	PSG
private:BioSerenity	BioSerenity / SleepMed proprietary PSG collection (18,869 overnight recordings)	pretraining	PSG
MESA	Multi-Ethnic Study of Atherosclerosis (sleep ancillary, NSRR)	pretraining	PSG
MrOS	Outcomes of Sleep Disorders in Older Men (MrOS Sleep, NSRR)	pretraining	PSG

• Leave-one-out multimodal contrastive pretraining on the 'pretrain' split of the four cohorts (>585,000 h, ~65,000 participants); SHHS explicitly held out. Upstream model_base/config.json: mode=leave_one_out, epochs=1.
• Sources: Thapa et al., Nature Medicine 2026, doi:10.1038/s41591-025-04133-4, Results 'Dataset and SleepFM architecture'; https://github.com/zou-group/sleepfm-clinical README (main); braindecode SleepFM docstring (W/bd/braindecode/models/sleepfm.py).

Usage

from braindecode.models import SleepFM

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

Input must be sampled at 128 Hz; the reference patch_size=640 is a 5-second patch at that rate. A channel mask of shape (batch, n_chans) marks missing channels with True.

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.

Cite Braindecode

If you use this braindecode port in scientific work, cite the software on Zenodo, doi:10.5281/zenodo.17699192, and the original model and datasets cited above.

@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},
}