DS005642#

illusory-face-eeg

Access recordings and metadata through EEGDash.

Citation: Amanda K Robinson, Greta Stuart, Sophia M Shatek, Adrian Herbert, Jessica Taubert (2024). illusory-face-eeg. 10.18112/openneuro.ds005642.v1.0.1

Modality: eeg Subjects: 21 Recordings: 47 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005642

dataset = DS005642(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005642(cache_dir="./data", subject="01")

Advanced query

dataset = DS005642(
    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{ds005642,
  title = {illusory-face-eeg},
  author = {Amanda K Robinson and Greta Stuart and Sophia M Shatek and Adrian Herbert and Jessica Taubert},
  doi = {10.18112/openneuro.ds005642.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005642.v1.0.1},
}

About This Dataset#

EEG data for:

Robinson AK, Stuart G, Shatek SM, Herbert A, Taubert J. (2025). Neural correlates reveal separate stages of spontaneous face perception.

Preprint: https://doi.org/10.31234/osf.io/vrtbx_v1

300 images of human face, illusory face and matched non-face object stimuli. Three behavioural tasks and neural measurements (EEG) using these stimuli. - Spontaneous dissimilarity: triplet odd-one-out task - Face-like ratings: participants were asked to rate how easily they could see a face in the image, on a scale of 0-10. - Face/object discrimination: speeded categorisation task - EEG: Stimuli were presented centrally at 3.75 Hz, while participants performed an orthogonal target detection task

Stimuli, behavioural data and code available at GitHub: https://doi.org/10.5281/zenodo.15833508

Dataset Information#

Dataset ID

DS005642

Title

illusory-face-eeg

Year

2024

Authors

Amanda K Robinson, Greta Stuart, Sophia M Shatek, Adrian Herbert, Jessica Taubert

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005642.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005642,
  title = {illusory-face-eeg},
  author = {Amanda K Robinson and Greta Stuart and Sophia M Shatek and Adrian Herbert and Jessica Taubert},
  doi = {10.18112/openneuro.ds005642.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005642.v1.0.1},
}

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: 21

  • Recordings: 47

  • Tasks: 1

Channels & sampling rate

  • Channels: 68

  • Sampling rate (Hz): 1024.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 13.8 GB

  • File count: 47

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005642.v1.0.1

Provenance

API Reference#

Use the DS005642 class to access this dataset programmatically.

class eegdash.dataset.DS005642(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005642. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 21; 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/ds005642 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005642

Examples

>>> from eegdash.dataset import DS005642
>>> dataset = DS005642(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#