DS004626: eeg dataset, 52 subjects#

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: 52 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.
Author (year)	`Maka2023`
Canonical	—
Importable as	`DS004626`, `Maka2023`
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

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: 52
Tasks: 1

Channels & sampling rate

Channels: 68
Sampling rate (Hz): 1000.0
Duration (hours): 21.358625555555555

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=52, range 19–35 yr)

1520253035

Sex distribution

32

20

Female Male Total: 52

Channel counts: 68 ch (n=52 recordings)

Sampling frequencies: 1000.0 Hz (n=52 recordings)

Total recording duration: 21 h 21 min

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Files: —

Size: —

Subjects: —

Click to load file structure…

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

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.

Study:: ds004626 (OpenNeuro)
Author (year):: Maka2023
Canonical:: —

Also importable as: DS004626, Maka2023.

Modality: eeg. 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 DOI: https://doi.org/10.18112/openneuro.ds004626.v1.0.2 NEMAR citation count: 1

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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)