DS005107: meg dataset, 21 subjects#

FACE-DEC

Access recordings and metadata through EEGDash.

Citation: Wei Xu, et al. (2024). FACE-DEC. 10.18112/openneuro.ds005107.v2.0.0

Modality: meg Subjects: 21 Recordings: 350 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005107

dataset = DS005107(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005107(cache_dir="./data", subject="01")

Advanced query

dataset = DS005107(
    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{ds005107,
  title = {FACE-DEC},
  author = {Wei Xu and et al.},
  doi = {10.18112/openneuro.ds005107.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005107.v2.0.0},
}

About This Dataset#

Main entrance: face_0_main Preprocessing: face_1_prep Decoding: face_2_dec RSA: face_3_rsa Statistical: face_4_stat BMS: face_6_bayes.m During original OPM-MEG data acquisition, individual facial point clouds and structural MRIs were not collected due to the unavailability of optical scanning equipment. Hence, all analyses were conducted at the whole-brain & sensor level. The raw data were originally stored using in-house LabVIEW format and were converted into the FIF format later. It should be noted that the sensor coordinates used were approximated by selecting corresponding locations from the Elekta layout and do not reflect the actual sensor positions (only for visualizing topographic maps). We are currently checking all the data to ensure that everything has been uploaded correctly. :) Correspondence: weixu@mail.bnu.edu.cn

Dataset Information#

Dataset ID

DS005107

Title

FACE-DEC

Author (year)

Xu2024_DEC

Canonical

—

Importable as

DS005107, Xu2024_DEC

Year

2024

Authors

Wei Xu, et al.

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005107.v2.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005107,
  title = {FACE-DEC},
  author = {Wei Xu and et al.},
  doi = {10.18112/openneuro.ds005107.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005107.v2.0.0},
}

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: 21

  • Recordings: 350

  • Tasks: 1

Channels & sampling rate

  • Channels: 65

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 31.625122222222224

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 27.6 GB

  • File count: 350

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005107.v2.0.0

Provenance

API Reference#

Use the DS005107 class to access this dataset programmatically.

class eegdash.dataset.DS005107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

FACE-DEC

Study:

ds005107 (OpenNeuro)

Author (year):

Xu2024_DEC

Canonical:

—

Also importable as: DS005107, Xu2024_DEC.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 350; 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/ds005107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005107 DOI: https://doi.org/10.18112/openneuro.ds005107.v2.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005107
>>> dataset = DS005107(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#