DS004980: eeg dataset, 17 subjects#

EEG data set for a architectural affordances task

Access recordings and metadata through EEGDash.

Citation: Wang,S., Oliveira,G.S., Djebbara,Z, Gramann, K. (2024). EEG data set for a architectural affordances task. 10.18112/openneuro.ds004980.v1.0.0

Modality: eeg Subjects: 17 Recordings: 17 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004980

dataset = DS004980(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004980(cache_dir="./data", subject="01")

Advanced query

dataset = DS004980(
    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{ds004980,
  title = {EEG data set for a architectural affordances task},
  author = {Wang,S. and Oliveira,G.S. and Djebbara,Z and Gramann, K.},
  doi = {10.18112/openneuro.ds004980.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004980.v1.0.0},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

Dataset ID

DS004980

Title

EEG data set for a architectural affordances task

Author (year)

Wang2024_architectural_affordances

Canonical

—

Importable as

DS004980, Wang2024_architectural_affordances

Year

2024

Authors

Wang,S., Oliveira,G.S., Djebbara,Z, Gramann, K.

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004980.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004980,
  title = {EEG data set for a architectural affordances task},
  author = {Wang,S. and Oliveira,G.S. and Djebbara,Z and Gramann, K.},
  doi = {10.18112/openneuro.ds004980.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004980.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: 17

  • Recordings: 17

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 500.0 (4), 499.9914353, 499.9919367, 499.9923017, 499.9923795, 499.9917272, 499.9914553, 499.9919292, 499.9917286, 499.9911824, 499.9912809, 499.9917378, 499.991385, 499.9915179

  • Duration (hours): 36.846338890955984

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 15.8 GB

  • File count: 17

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004980.v1.0.0

Provenance

API Reference#

Use the DS004980 class to access this dataset programmatically.

class eegdash.dataset.DS004980(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG data set for a architectural affordances task

Study:

ds004980 (OpenNeuro)

Author (year):

Wang2024_architectural_affordances

Canonical:

—

Also importable as: DS004980, Wang2024_architectural_affordances.

Modality: eeg. Subjects: 17; recordings: 17; 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/ds004980 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004980 DOI: https://doi.org/10.18112/openneuro.ds004980.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004980
>>> dataset = DS004980(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#