DS004995: eeg dataset, 20 subjects#

The Time-Course of Food Representation in the Human Brain

Access recordings and metadata through EEGDash.

Citation: Denise Moerel, James Psihoyos, Thomas A. Carlson (2024). The Time-Course of Food Representation in the Human Brain. 10.18112/openneuro.ds004995.v1.0.2

Modality: eeg Subjects: 20 Recordings: 20 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004995

dataset = DS004995(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004995(cache_dir="./data", subject="01")

Advanced query

dataset = DS004995(
    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{ds004995,
  title = {The Time-Course of Food Representation in the Human Brain},
  author = {Denise Moerel and James Psihoyos and Thomas A. Carlson},
  doi = {10.18112/openneuro.ds004995.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004995.v1.0.2},
}

About This Dataset#

The main folder contains the raw EEG data in standard bids format. See references. Code and figures: https://doi.org/10.17605/OSF.IO/PWC4K Manuscript: https://doi.org/10.1101/2023.06.06.543985 References: Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Dataset Information#

Dataset ID

DS004995

Title

The Time-Course of Food Representation in the Human Brain

Author (year)

Moerel2024

Canonical

Moerel2023

Importable as

DS004995, Moerel2024, Moerel2023

Year

2024

Authors

Denise Moerel, James Psihoyos, Thomas A. Carlson

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004995.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004995,
  title = {The Time-Course of Food Representation in the Human Brain},
  author = {Denise Moerel and James Psihoyos and Thomas A. Carlson},
  doi = {10.18112/openneuro.ds004995.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004995.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: 20

  • Recordings: 20

  • Tasks: 1

Channels & sampling rate

  • Channels: 127

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 16.189522222222223

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 27.6 GB

  • File count: 20

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004995.v1.0.2

Provenance

API Reference#

Use the DS004995 class to access this dataset programmatically.

class eegdash.dataset.DS004995(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

The Time-Course of Food Representation in the Human Brain

Study:

ds004995 (OpenNeuro)

Author (year):

Moerel2024

Canonical:

Moerel2023

Also importable as: DS004995, Moerel2024, Moerel2023.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 20; 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/ds004995 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004995 DOI: https://doi.org/10.18112/openneuro.ds004995.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004995
>>> dataset = DS004995(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#