DS004626#

Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and event-related potentials.

Access recordings and metadata through EEGDash.

Citation: Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek (2023). Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and event-related potentials.. 10.18112/openneuro.ds004626.v1.0.2

Modality: eeg Subjects: 52 Recordings: 266 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004626

dataset = DS004626(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004626(cache_dir="./data", subject="01")

Advanced query

dataset = DS004626(
    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{ds004626,
  title = {Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and event-related potentials.},
  author = {Szymon Mąka and Marta Chrustowicz and Łukasz Okruszek},
  doi = {10.18112/openneuro.ds004626.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004626.v1.0.2},
}

About This Dataset#

Dataset is related to publication: Mąka, S., Chrustowicz, M., & Okruszek, Ł. (2023). Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modeling and event-related potentials. Psychophysiology, e14406. https://doi. org/10.1111/psyp.14406

Dataset Information#

Dataset ID

DS004626

Title

Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and event-related potentials.

Year

2023

Authors

Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004626.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004626,
  title = {Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and event-related potentials.},
  author = {Szymon Mąka and Marta Chrustowicz and Łukasz Okruszek},
  doi = {10.18112/openneuro.ds004626.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004626.v1.0.2},
}

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: 52

  • Recordings: 266

  • Tasks: 1

Channels & sampling rate

  • Channels: 68

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 19.9 GB

  • File count: 266

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004626.v1.0.2

Provenance

API Reference#

Use the DS004626 class to access this dataset programmatically.

class eegdash.dataset.DS004626(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004626. Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 52; recordings: 52; 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/ds004626 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004626

Examples

>>> from eegdash.dataset import DS004626
>>> dataset = DS004626(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#