DS004802#

Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness

Access recordings and metadata through EEGDash.

Citation: Joe Bathelt, Marte Otten (2023). Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness. 10.18112/openneuro.ds004802.v1.0.0

Modality: eeg Subjects: 38 Recordings: 139 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004802

dataset = DS004802(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004802(cache_dir="./data", subject="01")

Advanced query

dataset = DS004802(
    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{ds004802,
  title = {Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness},
  author = {Joe Bathelt and Marte Otten},
  doi = {10.18112/openneuro.ds004802.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004802.v1.0.0},
}

About This Dataset#

Pilot EEG data for the registered report ‘Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness’. For further details, please see https://osf.io/c2svz/?view_only=4ee744ac88c74f41a4d955824a69284b

Dataset Information#

Dataset ID

DS004802

Title

Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness

Year

2023

Authors

Joe Bathelt, Marte Otten

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004802.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004802,
  title = {Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness},
  author = {Joe Bathelt and Marte Otten},
  doi = {10.18112/openneuro.ds004802.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004802.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: 38

  • Recordings: 139

  • Tasks: 1

Channels & sampling rate

  • Channels: 65

  • Sampling rate (Hz): 512.0 (54), 2048.0 (22)

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 10.1 GB

  • File count: 139

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004802.v1.0.0

Provenance

API Reference#

Use the DS004802 class to access this dataset programmatically.

class eegdash.dataset.DS004802(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004802. Modality: eeg; Experiment type: Affect; Subject type: Other. Subjects: 39; recordings: 79; 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/ds004802 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004802

Examples

>>> from eegdash.dataset import DS004802
>>> dataset = DS004802(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#