DS003474#

EEG: Probabilistic Selection and Depression

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh jcavanagh@unm.edu (2021). EEG: Probabilistic Selection and Depression. 10.18112/openneuro.ds003474.v1.1.0

Modality: eeg Subjects: 122 Recordings: 1014 License: CC0 Source: openneuro Citations: 9.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003474

dataset = DS003474(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003474(cache_dir="./data", subject="01")

Advanced query

dataset = DS003474(
    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{ds003474,
  title = {EEG: Probabilistic Selection and Depression},
  author = {James F Cavanagh jcavanagh@unm.edu},
  doi = {10.18112/openneuro.ds003474.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003474.v1.1.0},
}

About This Dataset#

Probabilistic selection task with 122 college-age participants. Task included in DMDX programming language. Data collected circa 2008-2010 in John J.B. Allen lab at U Arizona. Subjects scored reliably high or low in Beck Depression Inventory. Some have been clinically interviewed. For some subjects (maybe all?), HEOG and VEOG may be mis-labeled as the other. Some files have had some channels interpolated already. There are no raw data to revert to instead… Note subj 544 is not used b/c they had unstable BDI from pre-assessment to test session. Code is included to re-create this paper: DOI: 10.1162/cpsy_a_00024

  • James F Cavanagh 01/11/2021

Dataset Information#

Dataset ID

DS003474

Title

EEG: Probabilistic Selection and Depression

Year

2021

Authors

James F Cavanagh jcavanagh@unm.edu

License

CC0

Citation / DOI

10.18112/openneuro.ds003474.v1.1.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003474,
  title = {EEG: Probabilistic Selection and Depression},
  author = {James F Cavanagh jcavanagh@unm.edu},
  doi = {10.18112/openneuro.ds003474.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003474.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: 122

  • Recordings: 1014

  • Tasks: 1

Channels & sampling rate

  • Channels: 64 (122), 66 (72), 67 (50)

  • Sampling rate (Hz): 500.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 16.6 GB

  • File count: 1014

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds003474.v1.1.0

Provenance

API Reference#

Use the DS003474 class to access this dataset programmatically.

class eegdash.dataset.DS003474(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003474. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 122; recordings: 122; 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/ds003474 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003474

Examples

>>> from eegdash.dataset import DS003474
>>> dataset = DS003474(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#