# Multi-channel EEG recordings during a sustained-attention driving task (nm000275)

## Use this data

### What it is

- **Modalities:** EEG
- **Participants:** 27
- **Size:** 36.4 GB
- **Tasks:** driving
- **HED version:** 8.2.0

### License and terms

- **License:** CC-BY-4.0
- **Recommended citation:** Cao, Z., Chuang, C., King, J., & Lin, C. (2026). Multi-channel EEG recordings during a sustained-attention driving task (Version v1.0.0) \[Data set\]. NEMAR. https://doi.org/10.82901/nemar.nm000275
- **Reference 1:** [https://doi.org/10.1038/s41597-019-0027-4](https://doi.org/10.1038/s41597-019-0027-4)
- **Reference 2:** [https://doi.org/10.6084/m9.figshare.7666055.v3](https://doi.org/10.6084/m9.figshare.7666055.v3)
- **Reference 3:** [https://doi.org/10.1016/j.neuroimage.2014.01.015](https://doi.org/10.1016/j.neuroimage.2014.01.015)
- **Reference 4:** [https://doi.org/10.1038/srep21353](https://doi.org/10.1038/srep21353)
- **Reference 5:** [https://doi.org/10.1109/TBCAS.2014.2316224](https://doi.org/10.1109/TBCAS.2014.2316224)
- **Reference 6:** [https://doi.org/10.1109/TNNLS.2013.2275003](https://doi.org/10.1109/TNNLS.2013.2275003)
- **Reference 7:** [https://doi.org/10.1016/j.knosys.2015.01.007](https://doi.org/10.1016/j.knosys.2015.01.007)
- **Reference 8:** [https://doi.org/10.1109/TNNLS.2015.2496330](https://doi.org/10.1109/TNNLS.2015.2496330)
- **Reference 9:** [https://doi.org/10.1109/TFUZZ.2016.2633379](https://doi.org/10.1109/TFUZZ.2016.2633379)

### Where the bytes are

- **Latest version (always current):** [https://data.nemar.org/nm000275/latest/](https://data.nemar.org/nm000275/latest/)
- **This version (v1.0.0):** [https://data.nemar.org/nm000275/v1.0.0/](https://data.nemar.org/nm000275/v1.0.0/)

### How to download

- **The dataset:** `nemar dataset download nm000275`. 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 nm000275 --subjects sub-01,02`. Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
- **A subset, step 1:** `nemar dataset clone nm000275`. Clones git-annex pointers only; fetches no file content. Creates ./nm000275.
- **A subset, step 2:** `cd nm000275`. 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/nm000275/v1.0.0/participants.tsv](https://data.nemar.org/nm000275/v1.0.0/participants.tsv). A direct HTTPS fetch works for any single file.

### Assess fit without downloading

- **Participants table:** [https://data.nemar.org/nm000275/v1.0.0/participants.tsv](https://data.nemar.org/nm000275/v1.0.0/participants.tsv)
- **Dataset description:** [https://data.nemar.org/nm000275/v1.0.0/dataset_description.json](https://data.nemar.org/nm000275/v1.0.0/dataset_description.json)
- **Directory listing:** [https://data.nemar.org/nm000275/v1.0.0/?format=json](https://data.nemar.org/nm000275/v1.0.0/?format=json)
- **Catalog record:** [https://api.nemar.org/datasets/nm000275](https://api.nemar.org/datasets/nm000275)

### Working with the Zarr copy

- **1. Start at the index:** [https://zarr.nemar.org/nm000275/zarr/index.json](https://zarr.nemar.org/nm000275/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/nm000275/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).