NM000271: eeg dataset, 28 subjects#

chang2025 - NEMAR Dataset

Access recordings and metadata through EEGDash.

Citation: Unknown (—). chang2025 - NEMAR Dataset.

Modality: eeg Subjects: 28 Recordings: 1245 License: — Source: nemar

Metadata: Partial (60%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000271

dataset = NM000271(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000271(cache_dir="./data", subject="01")

Advanced query

dataset = NM000271(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000271,
  title = {chang2025 - NEMAR Dataset},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

Dataset ID

NM000271

Title

chang2025 - NEMAR Dataset

Author (year)

Chang2025_2

Canonical

Chang2025

Importable as

NM000271, Chang2025_2, Chang2025

Year

—

Authors

Unknown

License

—

Citation / DOI

Unknown

Source links

OpenNeuro | NeMAR | Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 28

  • Recordings: 1245

  • Tasks: 3

Channels & sampling rate

  • Channels: 59

  • Sampling rate (Hz): 1000

  • Duration (hours): 5.824969444444444

Tags

  • Pathology: Not specified

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: —

  • File count: 1245

  • Format: BIDS

License & citation

  • License: See source

  • DOI: —

Provenance

API Reference#

Use the NM000271 class to access this dataset programmatically.

class eegdash.dataset.NM000271(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

chang2025 - NEMAR Dataset

Study:

nm000271 (NeMAR)

Author (year):

Chang2025_2

Canonical:

Chang2025

Also importable as: NM000271, Chang2025_2, Chang2025.

Modality: eeg; Experiment type: Motor; Subject type: Unknown. Subjects: 28; recordings: 1245; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000271 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000271

Examples

>>> from eegdash.dataset import NM000271
>>> dataset = NM000271(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#