DS005087#

rapid-hemifield-object-eeg

Access recordings and metadata through EEGDash.

Citation: Amanda K Robinson, Tijl Grootswagers, Sophia M Shatek, Marlene Behrmann, Thomas A Carlson (2024). rapid-hemifield-object-eeg. 10.18112/openneuro.ds005087.v1.0.1

Modality: eeg Subjects: 20 Recordings: 207 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005087

dataset = DS005087(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005087(cache_dir="./data", subject="01")

Advanced query

dataset = DS005087(
    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{ds005087,
  title = {rapid-hemifield-object-eeg},
  author = {Amanda K Robinson and Tijl Grootswagers and Sophia M Shatek and Marlene Behrmann and Thomas A Carlson},
  doi = {10.18112/openneuro.ds005087.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005087.v1.0.1},
}

About This Dataset#

Object and word stimuli presented at 5Hz to the left or right visual fields, or centrally, while participants performed an orthogonal red target detection task

[PUBLICATION] Robinson A.K., Grootswagers T., Shatek S., Behrmann M., Carlson T.A. (2025). Dynamics of visual object coding within and across the hemispheres: Objects in the periphery. Science Advances, 11, eadq0889, https://doi.org/10.1126/sciadv.adq0889

Dataset Information#

Dataset ID

DS005087

Title

rapid-hemifield-object-eeg

Year

2024

Authors

Amanda K Robinson, Tijl Grootswagers, Sophia M Shatek, Marlene Behrmann, Thomas A Carlson

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005087.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005087,
  title = {rapid-hemifield-object-eeg},
  author = {Amanda K Robinson and Tijl Grootswagers and Sophia M Shatek and Marlene Behrmann and Thomas A Carlson},
  doi = {10.18112/openneuro.ds005087.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005087.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: 20

  • Recordings: 207

  • Tasks: 1

Channels & sampling rate

  • Channels: 63

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 12.2 GB

  • File count: 207

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005087.v1.0.1

Provenance

API Reference#

Use the DS005087 class to access this dataset programmatically.

class eegdash.dataset.DS005087(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005087. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 60; tasks: 3.

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/ds005087 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005087

Examples

>>> from eegdash.dataset import DS005087
>>> dataset = DS005087(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#