DS002712#

Numbers and Letters

Access recordings and metadata through EEGDash.

Citation: Sara Aurtenetxe, Nicola Molinaro, Doug Davidson, Manuel Carreiras (2020). Numbers and Letters. 10.18112/openneuro.ds002712.v1.0.1

Modality: meg Subjects: 25 Recordings: 482 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002712

dataset = DS002712(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS002712(cache_dir="./data", subject="01")

Advanced query

dataset = DS002712(
    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{ds002712,
  title = {Numbers and Letters},
  author = {Sara Aurtenetxe and Nicola Molinaro and Doug Davidson and Manuel Carreiras},
  doi = {10.18112/openneuro.ds002712.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds002712.v1.0.1},
}

About This Dataset#

OpenNeuro curator note: This dataset was previously accessible at ds001985. The dataset was reuploaded due to privacy considerations.

The experiment is composed by two runs We here report the code triggers for each run:

Run 1: single item 10 = single numbers 15 = single letters 20 & 25 = single false fonts

Run 2: strings 35 = strings numbers 40 = strings letters 45 & 50 = strings false fonts

raw files could be split into two files (e.g., run-1 + run-11)

Dataset Information#

Dataset ID

DS002712

Title

Numbers and Letters

Year

2020

Authors

Sara Aurtenetxe, Nicola Molinaro, Doug Davidson, Manuel Carreiras

License

CC0

Citation / DOI

10.18112/openneuro.ds002712.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds002712,
  title = {Numbers and Letters},
  author = {Sara Aurtenetxe and Nicola Molinaro and Doug Davidson and Manuel Carreiras},
  doi = {10.18112/openneuro.ds002712.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds002712.v1.0.1},
}

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: 25

  • Recordings: 482

  • Tasks: 1

Channels & sampling rate

  • Channels: 306 (79), 312 (79), 361 (2), 52 (2), 5, 314

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 101.8 GB

  • File count: 482

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds002712.v1.0.1

Provenance

API Reference#

Use the DS002712 class to access this dataset programmatically.

class eegdash.dataset.DS002712(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002712. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 25; recordings: 82; 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/ds002712 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002712

Examples

>>> from eegdash.dataset import DS002712
>>> dataset = DS002712(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#