DS004817#

EEG-attention-rsvp-exp2

Access recordings and metadata through EEGDash.

Citation: Grootswagers, Tijl, Robinson, Amanda, Shatek, Sofia, Carlson, Thomas (2023). EEG-attention-rsvp-exp2. 10.18112/openneuro.ds004817.v1.0.1

Modality: eeg Subjects: 20 Recordings: 83 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004817

dataset = DS004817(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004817(cache_dir="./data", subject="01")

Advanced query

dataset = DS004817(
    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{ds004817,
  title = {EEG-attention-rsvp-exp2},
  author = {Grootswagers, Tijl and Robinson, Amanda and Shatek, Sofia and Carlson, Thomas},
  doi = {10.18112/openneuro.ds004817.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004817.v1.0.1},
}

About This Dataset#

EEG data for Grootswagers et al 2021 experiment 2 (small objects on big letters) Grootswagers T., Robinson A.K., Shatek S.M., Carlson T.A. (2021). The neural dynamics underlying prioritisation of task-relevant information. Neurons, Behaviour, Data Analysis, and Theory, 5(1) https://doi.org/10.51628/001c.19129 See also https://osf.io/7zhwp/ and https://openneuro.org/datasets/ds004816

Dataset Information#

Dataset ID

DS004817

Title

EEG-attention-rsvp-exp2

Year

2023

Authors

Grootswagers, Tijl, Robinson, Amanda, Shatek, Sofia, Carlson, Thomas

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004817.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004817,
  title = {EEG-attention-rsvp-exp2},
  author = {Grootswagers, Tijl and Robinson, Amanda and Shatek, Sofia and Carlson, Thomas},
  doi = {10.18112/openneuro.ds004817.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004817.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: 83

  • Tasks: 1

Channels & sampling rate

  • Channels: 63

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 10.1 GB

  • File count: 83

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004817.v1.0.1

Provenance

API Reference#

Use the DS004817 class to access this dataset programmatically.

class eegdash.dataset.DS004817(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004817. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; 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/ds004817 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004817

Examples

>>> from eegdash.dataset import DS004817
>>> dataset = DS004817(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#