nm000287 NEMAR-native dataset

Muse Sleep-Onset EEG — EEG/EMG Foundation Challenge 2026, Track 03

This dataset contains 540 at-home, four-channel (TP9, AF7, AF8, TP10) EEG recordings from 203 participants, collected with Muse headbands at 128 Hz. It was prepared for Track 03 (Sleep onset) of the EEG/EMG Foundation Challenge 2026, where the task is to predict time remaining until the first N2 sleep epoch from EEG recorded up to the prediction time. Each recording carries a single N2-onset annotation, and the deposit includes a train/test split and signal-quality flags.

EEG Zarr

Issues GitHub

Download this dataset

Pick a method. Large datasets skip the zip and use the streaming methods below — all resumable. Full download guide →

Download archive (.zip) — 1.1 GB
A single zip of the published version. Best for small/medium datasets.

Download zip
NEMAR CLI recommended
Pulls the pinned version + annexed data and resumes cleanly. Install nemar-cli →
```
nemar dataset download nm000287
```

DataLad

Clone the dataset repo and fetch file content on demand. Docs →

datalad clone https://github.com/nemarDatasets/nm000287 nm000287
cd nm000287 && datalad get .

git-annex

Plain git + git-annex against the dataset repo. Docs →

git clone https://github.com/nemarDatasets/nm000287 nm000287
cd nm000287 && git annex get .

Direct files (wget / curl / rclone)
Every file with a stable, range-resumable URL from the manifest. Needs curl, jq, wget (or rclone/aria2c). Docs →
```
curl -s https://data.nemar.org/nm000287/v1.0.0/manifest.json | jq -r '.[].bytes_url' > urls.txt
wget -xc -i urls.txt
```

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

NeuroScience Gateway (NSG) portal.
NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/nm000287), and submit.
nsgportal.org →
Local processing with nemar-cli.
Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning.
```
npm install -g nemar-cli
nemar dataset clone nm000287
cd nm000287 && nemar dataset get
```
Just the files.
rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000287/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.

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Files

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How to use the data (for agentic research) license, citation, download commands, Zarr access

What it is

Modalities: EEG
Participants: 203
Size: 1.08 GB
Tasks: sleeponset

License and terms

License: CC-BY-NC-SA-4.0
Note: Non-commercial use only (CC-BY-NC-SA-4.0).
Recommended citation: Team, M. (2026). Muse Sleep-Onset EEG — EEG/EMG Foundation Challenge 2026, Track 03 (Version v1.0.0) [Data set]. NEMAR. https://doi.org/10.82901/nemar.nm000287
Reference 1: https://doi.org/10.21105/joss.01896
Reference 2: https://doi.org/10.1038/s41597-019-0104-8

Where the bytes are

Latest version (always current): https://data.nemar.org/nm000287/latest/
This version (v1.0.0): https://data.nemar.org/nm000287/v1.0.0/

How to download

The dataset: nemar dataset download nm000287 Clones and fetches in one step. Content under stimuli/ and derivatives/ is skipped by default because those trees can be large; add --stimuli --derivatives for the whole thing.
A subset, one step: nemar dataset download nm000287 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
A subset, step 1: nemar dataset clone nm000287 Clones git-annex pointers only; fetches no file content. Creates ./nm000287.
A subset, step 2: cd nm000287 The get command below reads the clone's annex, so it only works from inside the clone.
A subset, step 3: nemar dataset get <files> Pulls the files you actually need. Skips stimuli/ and derivatives/ unless the path you ask for is under one of them.
One small file: https://data.nemar.org/nm000287/v1.0.0/participants.tsv A direct HTTPS fetch works for any single file.

Assess fit without downloading

Participants table: https://data.nemar.org/nm000287/v1.0.0/participants.tsv
Dataset description: https://data.nemar.org/nm000287/v1.0.0/dataset_description.json
Directory listing: https://data.nemar.org/nm000287/v1.0.0/?format=json
Catalog record: https://api.nemar.org/datasets/nm000287

Working with the Zarr copy

1. Start at the index: https://zarr.nemar.org/nm000287/zarr/index.json The mandatory entry point. Never hardcode a bucket path.
2. Pick a store entry: stores[].zarr, stores[].groups[].name These two fields exist in every index format version, so a recipe that keys on them works against the whole catalog while the back conversion is still in flight.
3. Build the store URI: s3://nemar/nm000287/zarr/{store.zarr} Derivable from the store entry alone. An index at format_version 3 or later also publishes contract_base, data_base and s3_uri; use them when they are there, never require them.
4. Open the store anonymously: zarr.open_group(store=..., mode="r", zarr_format=3) Anonymous FsspecStore.from_url in region us-east-2, no credentials. zarr_format=3 is required: without it zarr-python probes for Zarr v2 sidecars, and because anonymous ListBucket is denied, S3 answers a missing key with 403 rather than 404 and the open raises.
5. Read the level-0 array: root[store.groups[0].name]["0"] Level 0 is the full-rate signal. Never read a view/ array for inference; those exist for display.
6. Dequantize the samples: physical = digital * scale + offset scale and offset are attributes of the level-0 array, one entry per channel; the unit is on the group's channels attribute.
7. Slice, don't download: signal[0:4, 0:500] Stream a window of channels and samples; download only when you will touch most of the array.
8. Know the HTTP contract: index.json Only index.json is always proxied and edge-cached. A plain GET for a store object, manifest.json or events.parquet 302s to the public S3 object for non-browser clients, so follow redirects, and HEAD is never redirected.
9. Read the attribution before reuse: root.attrs["nemar"] The store carries its own dataset id, DOI, license, citation and source commit.
10. Filter for pipelines: has_zarr=1 This is the converted filter. has_zarr_verified is the stricter one, and its result set can be empty until the daily fidelity sweep reaches a dataset; verification is reported, never a precondition for serving (nemar-cli ADR 0005).