DS006866#

Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals

Access recordings and metadata through EEGDash.

Citation: Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek (2025). Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals. 10.18112/openneuro.ds006866.v1.0.0

Modality: eeg Subjects: 148 Recordings: 598 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006866

dataset = DS006866(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 = DS006866(cache_dir="./data", subject="01")

Advanced query

dataset = DS006866(
    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{ds006866,
  title = {Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals},
  author = {Szymon Mąka and Marta Chrustowicz and Łukasz Okruszek},
  doi = {10.18112/openneuro.ds006866.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006866.v1.0.0},
}

About This Dataset#

Emotion Processing and Regulation Task (Static Stimuli) — EEG Dataset

This dataset contains EEG recordings and behavioral data from the Emotion Processing and Regulation task with static emotional stimuli.

*Preregistration: https://osf.io/g8qey_ *Preprint: https://osf.io/preprints/psyarxiv/v9dt3_v2_

Dataset Information#

Dataset ID	`DS006866`
Title	Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals
Year	2025
Authors	Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006866.v1.0.0
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: 148
Recordings: 598
Tasks: 1

Channels & sampling rate

Channels: 69 (148), 66 (148)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006866 class to access this dataset programmatically.

class eegdash.dataset.DS006866(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006866. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 148; recordings: 148; 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/ds006866 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006866

Examples

>>> from eegdash.dataset import DS006866
>>> dataset = DS006866(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