eegdash.dataset.NM000107#

participants.tsv (OpenNeuro nm000107). Access recordings and metadata through EEGDash.

Modality: [‘emg’] Tasks: 0 License: CC-BY-NC 4.0 Subjects: 0 Recordings: 0 Source: nemar

Dataset Information#

Dataset ID

NM000107

Title

participants.tsv

Year

Unknown

Authors

Patrick Kaifosh, Thomas R. Reardon, CTRL-labs at Reality Labs

License

CC-BY-NC 4.0

Citation / DOI

10.5281/zenodo.17282507

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000107,
  title = {participants.tsv},
  author = {Patrick Kaifosh and Thomas R. Reardon and CTRL-labs at Reality Labs},
  doi = {10.5281/zenodo.17282507},
  url = {https://doi.org/10.5281/zenodo.17282507},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: Unknown

  • Sampling rate (Hz): Unknown

  • Duration (hours): 0

Tasks & conditions

  • Tasks: 0

  • Experiment type: Unknown

  • Subject type: Unknown

Files & format

  • Size on disk: Unknown

  • File count: Unknown

  • Format: Unknown

License & citation

  • License: CC-BY-NC 4.0

  • DOI: 10.5281/zenodo.17282507

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import NM000107

dataset = NM000107(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = NM000107(cache_dir="./data", subject="01")

dataset = NM000107(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

  • No dataset-specific caveats are listed in the available metadata.

API#

class eegdash.dataset.NM000107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset nm000107. Modality: emg; Experiment type: Unknown; Subject type: Unknown. Subjects: 100; recordings: 182; tasks: 1.

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/nm000107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000107 DOI: https://doi.org/10.5281/zenodo.17282507

Examples

>>> from eegdash.dataset import NM000107
>>> dataset = NM000107(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#