DS003519#

EEG: Visual Working Memory + Cabergoline Challenge

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Michael J Frank, James Broadway (2021). EEG: Visual Working Memory + Cabergoline Challenge. 10.18112/openneuro.ds003519.v1.1.0

Modality: eeg Subjects: 27 Recordings: 439 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003519

dataset = DS003519(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003519(cache_dir="./data", subject="01")

Advanced query

dataset = DS003519(
    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{ds003519,
  title = {EEG: Visual Working Memory + Cabergoline Challenge},
  author = {James F Cavanagh and Michael J Frank and James Broadway},
  doi = {10.18112/openneuro.ds003519.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003519.v1.1.0},
}

About This Dataset#

Visual Working Memory. Mostly unpublished! Beh data published here: 10.3758/s13415-018-0584-6. EEG data never published. Same sample as this published study: 10.1038/ncomms6394. 30 healthy participants (3 dropout) in a double-blind drug study. Total n=27. Drug was Cabergoline 1.25 mg. Subjects had IDs 305/405 - 330/430. The dual numbers were for session: 300s were first session, 400s were second session. Here we have simply put them in as session 1 and session 2. So Joe Smith would have been 305 on visit 1, then 405 on visit 2. If he got cab first we indicated that in the Sess1_Drug column. Task included in Matlab programming language. Data collected circa 2012-2013 in Laboratory for Neural Computation & Cognition at Brown. Check the .xls sheet under code folder for more meta data, incl. OSpan etc. - James F Cavanagh 02/15/2021

Dataset Information#

Dataset ID

DS003519

Title

EEG: Visual Working Memory + Cabergoline Challenge

Year

2021

Authors

James F Cavanagh, Michael J Frank, James Broadway

License

CC0

Citation / DOI

10.18112/openneuro.ds003519.v1.1.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003519,
  title = {EEG: Visual Working Memory + Cabergoline Challenge},
  author = {James F Cavanagh and Michael J Frank and James Broadway},
  doi = {10.18112/openneuro.ds003519.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003519.v1.1.0},
}

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: 27

  • Recordings: 439

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 500.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 9.0 GB

  • File count: 439

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds003519.v1.1.0

Provenance

API Reference#

Use the DS003519 class to access this dataset programmatically.

class eegdash.dataset.DS003519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003519. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 27; recordings: 54; 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/ds003519 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003519

Examples

>>> from eegdash.dataset import DS003519
>>> dataset = DS003519(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#