DS002908: meg dataset, 13 subjects#

Human MEG recordings during sequential conflict task

Access recordings and metadata through EEGDash.

Citation: Rafal Bogacz, Vladimir Litvak (2020). Human MEG recordings during sequential conflict task. 10.18112/openneuro.ds002908.v1.0.0

Modality: meg Subjects: 13 Recordings: 53 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002908

dataset = DS002908(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS002908(cache_dir="./data", subject="01")

Advanced query

dataset = DS002908(
    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{ds002908,
  title = {Human MEG recordings during sequential conflict task},
  author = {Rafal Bogacz and Vladimir Litvak},
  doi = {10.18112/openneuro.ds002908.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds002908.v1.0.0},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

Dataset ID

DS002908

Title

Human MEG recordings during sequential conflict task

Author (year)

Bogacz2020

Canonical

Bogacz2024

Importable as

DS002908, Bogacz2020, Bogacz2024

Year

2020

Authors

Rafal Bogacz, Vladimir Litvak

License

CC0

Citation / DOI

10.18112/openneuro.ds002908.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds002908,
  title = {Human MEG recordings during sequential conflict task},
  author = {Rafal Bogacz and Vladimir Litvak},
  doi = {10.18112/openneuro.ds002908.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds002908.v1.0.0},
}

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: 13

  • Recordings: 53

  • Tasks: 1

Channels & sampling rate

  • Channels: 299

  • Sampling rate (Hz): 2400.0

  • Duration (hours): 5.105833333333333

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: Attention

Files & format

  • Size on disk: 59.8 GB

  • File count: 53

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds002908.v1.0.0

Provenance

API Reference#

Use the DS002908 class to access this dataset programmatically.

class eegdash.dataset.DS002908(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Human MEG recordings during sequential conflict task

Study:

ds002908 (OpenNeuro)

Author (year):

Bogacz2020

Canonical:

Bogacz2024

Also importable as: DS002908, Bogacz2020, Bogacz2024.

Modality: meg; Experiment type: Attention; Subject type: Unknown. Subjects: 13; recordings: 53; 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/ds002908 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002908 DOI: https://doi.org/10.18112/openneuro.ds002908.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002908
>>> dataset = DS002908(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#