DS003655: eeg dataset, 156 subjects#

VerbalWorkingMemory

Access recordings and metadata through EEGDash.

Citation: Yuri G. Pavlov (2021). VerbalWorkingMemory. 10.18112/openneuro.ds003655.v1.0.0

Modality: eeg Subjects: 156 Recordings: 156 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003655

dataset = DS003655(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003655(cache_dir="./data", subject="01")

Advanced query

dataset = DS003655(
    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{ds003655,
  title = {VerbalWorkingMemory},
  author = {Yuri G. Pavlov},
  doi = {10.18112/openneuro.ds003655.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003655.v1.0.0},
}

About This Dataset#

EEG in a modified Sternberg working memory paradigm with two types of task: with mental manipulations (alphabetization) and simple retention (TASK) and 3 levels of load: 5, 6, or 7 letter to memorize (LOAD)

Dataset Information#

Dataset ID

DS003655

Title

VerbalWorkingMemory

Author (year)

Pavlov2021_VerbalWorkingMemory

Canonical

—

Importable as

DS003655, Pavlov2021_VerbalWorkingMemory

Year

2021

Authors

Yuri G. Pavlov

License

CC0

Citation / DOI

10.18112/openneuro.ds003655.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003655,
  title = {VerbalWorkingMemory},
  author = {Yuri G. Pavlov},
  doi = {10.18112/openneuro.ds003655.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003655.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: 156

  • Recordings: 156

  • Tasks: 1

Channels & sampling rate

  • Channels: 21

  • Sampling rate (Hz): 500.0

  • Duration (hours): 130.92305555555555

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 20.3 GB

  • File count: 156

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds003655.v1.0.0

Provenance

API Reference#

Use the DS003655 class to access this dataset programmatically.

class eegdash.dataset.DS003655(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

VerbalWorkingMemory

Study:

ds003655 (OpenNeuro)

Author (year):

Pavlov2021_VerbalWorkingMemory

Canonical:

—

Also importable as: DS003655, Pavlov2021_VerbalWorkingMemory.

Modality: eeg. Subjects: 156; recordings: 156; 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/ds003655 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003655 DOI: https://doi.org/10.18112/openneuro.ds003655.v1.0.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003655
>>> dataset = DS003655(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#